automated glaucoma detection

SIM UNIVERSITY

SCHOOL OF SCIENCE AND TECHNOLOGY

COMPUTER BASED DIAGNOSIS OF

GLAUCOMA USING PRINCIPAL

COMPONENT ANALYSIS (PCA): A

COMPARATIVE STUDY

STUDENT : Lee You Tai Danny (B0704498)

SUPERVISOR : Dr. Rajendra Acharya Udyavara

PROJECT CODE : JAN2011/ENG/015

A project report submitted to SIM University in partial fulfillment of the

requirements for the degree of Bachelor of Electronic Engineering

November 2011

JAN2011/ENG/015 LEE YOU TAI DANNY

(B0704498)

ABSTRACT

Glaucoma is one of the many eye diseases can lead to the blindness if it is not

detected and treated in proper time. It is often associated with the increased in the

intraocular pressure (IOP) of the fluid (known as aqueous humor) in the eye, and it has

been nicknamed as the “Silent Thief of Sight”. Glaucoma affects 40% of blindness in

Singapore and is the second leading cause of blindness in the world.

The detection of glaucoma through Optical Coherence Tomography (OCT) and

Heidelberg Retinal Tomography (HRT) is very expensive. This paper presents a novel

method to diagnose glaucoma using digital fundus images. Digital image processing

techniques, such as image pre-processing, texture features extraction are widely used for

the automatic detection of the various features. We have extracted features such as

Homogeneity, Energy, Contrast, Moments, Fractal Dimension, Local Binary Patterns,

Laws‟ Texture Energy and Fuzzy Gray Level Co-occurrence Matrix of the eye.

These features are validated by automatically classifying the normal and

glaucoma images using Probabilistic Neural Network (PNN) classifier. The images

were retrieved from the Kasturba Medical College, Manipal, India. The results

presented in this paper indicate that the features are clinically significant in the

diagnosis of glaucoma.

The overall objective is to apply image processing techniques on the digital

fundus images of the eye for the analysis of glaucoma and normal eye. In this study, for

each subject, 30 images were analyzed. By extracting information of pixel average

value from the images, it is possible to obtain the necessary value for classification.

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ACKNOWLEDGEMENTS

Firstly, I would like to express my sincere and heartfelt appreciation to my project

supervisor, Dr. Rajendra U.Acharya for his exceptional guidance, invaluable advice and

wholehearted support in matters of practical and theoretical nature throughout the

project. His constant time check and meet ups had certainly motivated me in the

completion of the project.

Throughout my thesis-writing period, he provided encouragement, sound advice, good

teaching, good company, and lots of good ideas. Thanks to him for his tolerance and

patience to all my queries regardless be it an email or phone call, he had almost

response to it with no delays. The completion of the Final Year Project would not be

possible without his excellence supervision.

And I also would like to extend my warm appreciation to my colleagues and friends for

sharing their knowledge, valuable contributions and help with this project.

Last but not least, I would like to thank to Kasturba Medical College, Manipal, India for

providing me with the digital fundus images which play major role in this Final Year

Project.

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LIST OF FIGURES

Figure 1.1: Proposed Design for a detection of Glaucoma ........................................................ 3

Figure 2.1: Simple diagram of the parts of the eye .................................................................... 5

Figure 2.2: Glaucoma eye anatomy ........................................................................................... 6

Figure 2.3: (a) Raw image before histogram equalization ......................................................... 9

Figure 2.3: (b) Pre-processed image and its histogram equalization ....................................... 10

Figure 4.1: (a) Fundus Camera and (b) Fundus Image ............................................................ 19

Figure 4.2: (a) Normal Eye Fundus Image and (b) Glaucomatous Eye Fundus Image ........... 20

Figure 4.3: (a) Normal FD and (b) Glaucoma FD ................................................................... 22

Figure 4.4: Circularly symmetric neighbor sets for different P and R ..................................... 23

Figure 4.5: Square neighborhood and Circular neighborhood ................................................. 23

Figure 4.6: Uniform and Non-uniform patterns ....................................................................... 24

Figure 5.1: Architecture of PNN .............................................................................................. 32

Figure 5.2: Procedure of three-fold stratified cross validation ................................................ 34

Figure 5.3: The distribution plot of the GII for normal and glaucoma subjects ...................... 36

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LIST OF TABLES

Table 3.1: Detail Project Plan .................................................................................................. 18

Table 4.1: LBP features for normal and glaucoma images with p-vale................................... 25

Table 4.2: LTE features of normal and glaucoma images with p-vale .................................... 27

Table 4.3: FGLCM features of normal and glaucoma images with p-vale.............................. 29

Table 5.1: 12 Features of normal and glaucoma PCA and their p-value ................................. 33

Table 5.2: PNN classification result......................................................................................... 34

Table 5.3: The GII values for normal and glaucoma subjects ................................................. 36

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TABLE OF CONTENTS

ABSTRACT ................................................................................................................................i

ACKNOWLEDGEMENTS ...................................................................................................... ii

LIST OF FIGURES ................................................................................................................. iii

LIST OF TABLES ....................................................................................................................iv

PART I .......................................................................................................................................1

CHAPTER 1: PROJECT INTRODUCTION ........................................................................1

1.1 Background and Motivation ........................................................................... 1

1.2 Project Objectives ........................................................................................... 2

1.3 Project Scope .................................................................................................. 2

CHAPTER 2: THEORY AND LITERATURE REVIEW ....................................................4

2.1 Anatomy of an Eye ......................................................................................... 4

2.2 Overview of Glaucoma ................................................................................... 5

2.3 Types of Glaucoma ......................................................................................... 6

2.3.1 Primary open angle glaucoma ...................................................................... 6

2.3.2 Angle closure glaucoma ............................................................................... 7

2.3.3 Secondary Glaucoma ................................................................................... 7

2.4 Detection of Glaucoma ................................................................................... 8

2.4 Image Processing with MATLAB .................................................................. 9

2.5 Statistical Application................................................................................... 10

2.5.1 Run length matrix....................................................................................... 14

CHAPTER 3: PROJECT MANAGEMENT........................................................................15

3.1 Project Plan ................................................................................................... 15

3.1.1 School facility ............................................................................................ 15

3.1.2 Internet broadband ..................................................................................... 15

3.2 Project Task and Schedule ............................................................................ 15

CHAPTER 4: DESIGN AND ALGORITHM .....................................................................19

4.1 Project Approach and Method ...................................................................... 19

4.2 Different Texture Features Study ................................................................. 21

4.2.1 Fractal Dimension ...................................................................................... 21

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4.2.2 Local Binary Patterns ................................................................................. 22

4.2.3 Laws‟ Texture Energy................................................................................ 26

4.2.4 Fuzzy Gray Level Co-occurrence Matrix .................................................. 28

CHAPTER 5: CLASSIFICATIONS AND RESULTS........................................................30

5.1 Principal Component Analysis (PCA) .......................................................... 30

5.2 Classifier Used .............................................................................................. 31

5.3 Results........................................................................................................... 32

5.4 Glaucoma Integrated Index........................................................................... 35

CHAPTER 6: DISCUSSION, CONCLUSION AND RECOMMENDATION ..................37

6.1 Discussion ..................................................................................................... 37

6.2 Conclusion .................................................................................................... 38

6.3 Recommendation .......................................................................................... 39

PART II ....................................................................................................................................40

CRITICAL REVIEW AND REFLECTION........................................................................40

REFERENCES.....................................................................................................................41

APPENDIXES .....................................................................................................................43

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PART I

CHAPTER 1: PROJECT INTRODUCTION

1.1 Background and Motivation

The eyes are the most complex, sensitive and delicate organs in our human body, which

we view the world and are responsible for four fifth of all the information our brain

receives. Blindness affects 3.3 million in America aged above 40 years and this may

reach 5.5 million by the year 2020. The most leading causes of the visual impairment

and blindness are cataract, glaucoma, macular degeneration and diabetes retinopathy.

Among these, Glaucoma affects more than 3 million people living in the United States

and is the leading cause for African Americans. Worldwide, it is the second leading

cause of blindness [Global data on visual impairment in the year 2002]. It affects one in

two hundred people aged fifty years and younger, and one in ten over the age of eighty

years.

[http://www.afb.org/seniorsite.asp?SectionID=63&TopicID=286&DocumentID=3198]

There is no cure for glaucoma yet, hence early detection and prevention is the

only way to treat glaucoma and avoid total loss of vision. Optical Coherence

Tomography (OCT) and Heidelberg Retinal Tomography (HRT) are used to

detect the glaucoma but the cost is very high. This paper presents a novel method

for glaucoma detection using digital fundus images. Digital image processing

techniques, morphological operations, histogram equalization, features

extraction, normalization, principal component analysis (PCA), statistical

analysis using Student T-Test and validated by classifying the normal and

glaucoma images using Probabilistic Neural Network (PNN).

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1.2 Project Objectives

This project requires the following tasks to achieve the main objective:

The main objective of this project is to analyze and diagnose the glaucoma using

digital fundus images by using image processing technique.

Detection and extraction of textures features and normalization of data.

Apply principal component analysis (PCA) to extract from these normalized

features.

Study various data mining techniques such as: K-Nearest Neighbor (K_NN),

Naïve Bayes Classifier (NBC) and Probabilistic Neural Network (PNN) for

classification.

The academic goal of this project is to develop the skill of research, MATLAB

programming and analysis.

1.3 Project Scope

The project will include the following proposed scheme:

Acquiring digital fundus images of normal and glaucoma eye with an age group

of 20 to 70 years. The images were collected from the Kasturba Medical

College, Manipal, India which photographed and certified by the doctors in the

ophthalmology department.

Image processing system which extract the relevant features for the automatic

diagnosis of the glaucoma.

Detection and extraction of various texture features.

Normalization and analysis of texture features using MATLAB and Principal

Component Analysis (PCA) method.

Classification of data using Probabilistic Neural Network (PNN).

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NORMAL GLAUCOMA

TEXTURE FEATURES EXTRACTION USING IMAGE PRE-PROCESSING

TECHNIQUES

STATISTICAL ANALYSIS USING

PRINCIPAL COMPONENT ANALYSIS (PCA) METHOD

CLASSIFICATION USING

PROBABILISTIC NEURAL NETWORK (PNN)

NNOORRMMAALL EEYYEE GLAUCOMA EYE

Figure 1.1: Proposed Design for a detection of Glaucoma

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CHAPTER 2: THEORY AND LITERATURE REVIEW

2.1 Anatomy of an Eye

Basically the human eye is an organ which reacts to light for many different purposes

and is made up of three coats, enclosing three transparent structures. The outermost

layer is composed of the cornea and sclera. The middle layer consists of the choroid,

ciliary body and iris. The innermost is the retina. The light rays from an object are

reflected off and enter through the cornea. It then refracts the rays that pass through the

pupil. Surrounding the pupils is the iris, the colored portion of the eye. The pupil opens

and closed to regulate the amount of light passing through it. Hence, we are able to see

the dilation of the pupils. Light rays will pass the lens that is located behind the pupils.

This lens change the shape of the rays by further bending and focusing them to the

retina located at the back of eyes.

The retina consists of two major types of light-sensitive receptors which are also called

tiny light-sensing nerve cells. They are cones nerve cells and rods nerve cells. The cones

enable us to see bright light which produces photonic vision. Cones are mostly

concentrated in and near the fovea. Only a few are present at the sides of the retina. It

gives a clear and sharp image vision as when one looks at an object directly.

It detects colors and detects fine details. The rods however, enable us to see in the dark

which produces isotopic vision. It detects motion in the dark. It is located outside the

macula and goes all the way to the outer of edge retina. Rod density is greater in the

peripheral retina than in the central retina. Hence it provides peripheral or side vision.

Cones and rods are connected through intermediate cells in the retina to nerve fibers of

the optic nerve. When rods and cones are stimulated by light, the nerves sendoff

impulses to the brain through these fibers. Figure 2.1 illustrate a simple diagram of the

parts of the eye.

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Figure 2.1: Simple diagram of the parts of the eye

2.2 Overview of Glaucoma

Glaucoma is an eye disease in which the optic nerve damages by the elevation in the

intraocular pressure inside the eye caused by a build-up of excess fluid. This pressure

can impair vision by causing irreversible damage to the optic nerve and to the retina. It

can lead to the blindness if it is not detected and treated in proper time. Glaucoma result

in peripheral vision loss, and is an especially dangerous eye condition because it

frequently progresses without obvious symptoms. This is why it is often referred to as

“The Silent Thief of Sight.”

There is no cure of glaucoma yet, although it can be treated. Worldwide, it is the second

leading cause of blindness [Global data on visual impairment in the year 2002]. It

affects one in two hundred people aged fifty years and younger, and one in ten over the

age of eighty years. The damage to the optic nerve from glaucoma cannot be reversed.

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However, lowering the pressure in the eye can prevent further damage to the optic nerve

and further peripheral vision loss.

There are various types of glaucoma that can occur and progress without obvious

symptoms or sign. Even there is no cure for glaucoma, early detection and prevention

can avoid total loss of vision. Glaucoma can be divided into two main types, (1)

Primary open angle glaucoma and (2) Angle closure glaucoma. Last but not least there

is another glaucoma known as secondary glaucoma, which will explain in next section.

Figure 2.2: Glaucoma eye anatomy

2.3 Types of Glaucoma

2.3.1 Primary open angle glaucoma

This type of glaucoma is the most common (sometimes called Chronic Glaucoma)

and symptoms are slow to develop. As the glaucoma progress the side or peripheral

vision is failing. It may cause a person to miss the objects out of the side or corner

of the eye. It happens when the eye‟s drainage canals become clogged over time or

the eye over-produces aqueous fluid which causes the pressure inside the eye to

build to abnormal levels. The inner eye pressure (IOP) rises because the correct

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amount of fluid can‟t drain out of the eye. It‟s affecting 70% to 80% of those who

suffered from the disorder and accounts for 90% of glaucoma cases in the United

States. It is painless and does not have acute attacks. It can develop gradually and go

unnoticed for many years but this type of glaucoma usually responds well to

medication, especially if caught early and treated.

2.3.2 Angle closure glaucoma

Also known as Acute Narrow Angle Glaucoma and accounts for less than 10% of

glaucoma cases in the United States. Although it is rare and is different from open

angle glaucoma it is the most serious form of disease. The problem occurs more

commonly in farsighted elderly people, particularly in women and often occurs in

both eyes. Angle closure glaucoma occurs primarily in patients who have a shallow

space between the cornea at the front of the eye and the colored iris that lies just

behind the cornea. As the eye ages, the pupil grows and becomes smaller, restricting

the flow of fluid to the drainage site. As fluid buildup and blockage happens, a rapid

rise in intraocular pressure can occur.

This kind of glaucoma is normally very painful because of the sudden increase in

pressure inside the eye. The symptoms of an acute attack are more severe and can be

totally disabling. They include severe pain, often accompanied by nausea and

vomiting. Diabetes can be a contributing cause to the development of glaucoma.

Treatment of angle closure glaucoma is known as peripheral iridectomy and usually

involves surgery to remove a small portion of the outer edge of the iris to allow

aqueous fluid to flow easily to the drainage site.

2.3.3 Secondary Glaucoma

Both open angle glaucoma and angle closure glaucoma can be primary or secondary

conditions. Primary conditions are when the cause is unknown, unlike secondary

conditions which can be traced to a known cause. Secondary glaucoma may be

caused by a variety of medical conditions, medications, eye abnormalities and

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physical injuries. The treatments of secondary glaucoma are frequently associated to

eye surgery.

Symptoms of glaucoma include:

Headaches

Intense pain

Blurred vision

Nausea or Vomiting

Medium dilation of the pupil

Bloodshot eyes and increased sensitivity

However, in general the field of vision is being narrowed to such whereby one is

unable to see clearly.

2.4 Detection of Glaucoma

There are three different tests which can detect the glaucoma:

Ophthalmoscopy

Tonometry

Perimetry

Two routine eye tests for regular glaucoma check-ups include: Tonometry and

Ophthalmoscopy. Most glaucoma tests are very time consuming and also need special

skills and diagnostic equipment. Therefore, new techniques to diagnose glaucoma at

early stages are urgently needed with accuracy, speed and even with less skilled people.

In recent year‟s computer based systems made glaucoma screening easier. Imaging

systems, such as fundus camera, optical coherence tomography (OCT), Heidelberg

retina tomography (HRT) and scanning laser polarimetry, have been extensively used

for eye diagnosis. HRT, confocal laser scanning tomography and OCT can show retina

nerve fiber damage even before the damage to the visual fields. However, those

equipment are very expensive and only some hospitals able to afford them. Hence

fundus cameras can be used as an alternative by many ophthalmologists to diagnose

glaucoma. Images processing technique allows to extract the features that can provide

useful information to diagnose glaucoma.

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2.4 Image Processing with MATLAB

MATLAB is high-level language and interactive environment that enables to perform

computationally intensive tasks faster than with traditional programming languages

such as C, C++, and FORTRAN. In this project, MATLAB is the main software to

implement the image processing part as well as texture features extraction and

classification. After texture features extraction, the data will be normalized and selected

Principal Component Analysis (PCA) features are fed to PNN classifier for

classification.

Image pre-processing is mainly to improve the image contrast using histogram

equalization whereas to increase the dynamic range of the histogram of an image and

intensity value of pixels in the input image. The output image contains a uniform

distribution of intensities and increased the contrast of an image. Figure 2.3: (a) and (b)

shows the raw image and pre-processed fundus image with histogram equalization

graph.

Figure 2.3: (a) Raw image before histogram equalization

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10000

9000

8000

7000

6000

5000

4000

3000

2000

1000

0

0 50 100 150 200 250

Figure 2.3: (b) Pre-processed image and its histogram equalization

2.5 Statistical Application

The statistical mathematical application is used in this project. The texture features were

extracted from each digital fundus image by image pre-processing techniques such as

Co-occurrence matrix, Contrast, Homogeneity, Entropy, Angular second moment,

Energy, Mean, Run length matrix, Short run emphasis, Long run emphasis, Run

Percentage, Gray Level Non-uniformity and Run Length Non-uniformity. The collected

data were normalized and selected using Principal Component Analysis (PCA). Finally

the selected PCA features were classified using Probabilistic Neural Network (PNN).

Co-occurrence matrix,

For an image of m x n, we can perform a second-order statistical textural analysis by

constructing the gray level co-occurrence matrix (GLCM) [Tan et al., 2009] by

Cd i, j

Where

p, q, p x, q y : I p, q i, I p x, q y j

p, q, p x, q y M N , d x, y

(2.1)

And denotes the cardinality of a set. For a pixel in an image having a grey level i, the

probability that the gray level of a pixel at a x, y distance away is j is defined as:

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Pd i, j Cd i, j

Cd i, j

(2.2)

With (1) and (2), we acquire the following features:

Energy: E P i, j 2

(2.3) i j

Energy is the sum of squared elements in the gray level co-occurrence matrix called

angular second moment. It is the measurement of the denseness or order in the image.

Contrast: Co i j Pd i, j (2.4) i j

Contrast is the differences in the gray level co-occurrence matrix or is the measurement

of the local variations. Its measure the elements do not lie on the main diagonal and

returns a measure of the intensity contrast between a pixel and the neighboring pixels

over the whole image.

Homogeneity: H Pd i, j

i j

(2.5)

Homogeneity measures the distribution of elements nearest to the diagonal in the

GLCM. It‟s inversely proportional to the contrast. The addition of value „1‟ in the

denominator is to prevent the value „0‟ during division.

Entropy: En Pd i, j ln Pd i, j i j

(2.6)

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Entropy is a thermodynamic property and its present the degree of disorder in the

image. The entropy value is the largest when all the elements of the co-occurrence

matrix are the same and small when elements are unequal.

Moments- m1 , m

2 , m3 and m

4 are defined as:

m i j g Pd i, j (2.7) i j

where g is the integer power exponent that defines the moment order. Moments are the

statistical expectation of certain power functions of a random variable that measure the

shape of a set of data points.

The first moment is the mean and which is the average of pixel values in an image.

The second moment is the standard deviation and is given by m2=E(x-µ)2

where E(x) is

the expected value of x. The standard deviation determines how much variation is from

the average or mean. It‟s denoted by Greek symbol σ.

The third moment measures the degree of asymmetry in the distribution. Also called

skewness and the value can be positive or negative or even undefined.

The fourth moment measures the relative peakedness or flatness of the probability

distribution of a real valued random variable and is also known as kurtosis. If a

distribution has a peak at the mean and long tails, the fourth moment will be high and

the kurtosis positive (platykurtic); and conversely, bounded distributions tend to have

low kurtosis (keptokurtic).

For difference statistics, which is a subset of co-occurrence matrix, it can be obtained by

[Tomita et al., 1990]:

P k Cd i, j i j

(2.8)

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Where i j k , k = 0, 1, … n – 1, and n is the number of gray scale level [9]. For each

entry in the difference matrix, it is in fact the sum of the probability that the gray-level

difference is k between two points separated by δ. We can derive the following

properties from the difference matrix [Weszka et al., 1976]:

n1

AngularSecondMoment : P (k )2

k 0

(2.9)

Angular second moment (also known as Uniformity) is large when certain values are

high and others are low. It‟s a measure of local homogeneity and the opposite of

Entropy.

n1

Contrast : k 2 P (k )

k 0

(2.10)

Contrast is the moment of inertia about the origin and also known as the second moment

of Pδ .

Entropy: n 1

P (k ) log P (k ) k 0

(2.11)

Entropy is directly proportional to unpredictability. Entropy is smallest when Pδ (k)

values are unequal and largest when Pδ (k) values are equal.

n 1

Mean: kP (k ) k 0

(2.12)

Mean is large when they are far from the origin and small when Pδ (k) values are

concentrated near the origin.

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2.5.1 Run length matrix

In run length matrix P

(i, j), each cell in the matrix consists of the number of elements

where gray level „i‟ successively appears „j‟ times in the direction . And the variable

„j‟ is termed as run length. The resultant matrix characterizes gray-level runs by the

gray tone, length and the direction of the run. This method allows extracting the higher

order statistical texture features. As a common practice, run length matrices of = 0,

45, 90 and 135 were calculated and get the following features [Galloway et al., 1975]:

Short run emphasis: P i, j 2

i j

P i, j i j

(2.13)

Long run emphasis: j 2 P i, j

i j

P i, j i j

(2.14)

2

Gray Level Non-uniformity: P i, j P i, j (2.15) i j i j

2

Run Length Non-uniformity: P i, j P i, j (2.16) j i i j

Run Percentage: P i, j A i j

(2.17)

where A is the area of interest in the image. This particular feature is the ratio between

the total number of observed runs in image and the total number of possible runs if all

runs had a length of one.

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CHAPTER 3: PROJECT MANAGEMENT

3.1 Project Plan

From the start of the project, proper planning is very important as it contributes the

success of the project. Time management factors play a critical role in every project and

it leads to a good graded project. I need to juggle between both my projects, exam and

my full-time job, having a well plan is not only my own contribution but also from my

project Supervisor, Dr. Rajendra Acharya U, did his part to meet up and constantly

reminding me as per the planned schedule. For the project to be made possible, a Gantt

chart (shown in appendix Q), was used to create the project plan and monitor the

progress.

3.1.1 School facility

The access to UniSIM library or any neighborhood library is a must. As most of my

research and read up on the various types of applications for image processing and

texture features extraction, came from the library shelf and online IEEE journals.

MATLAB Central also helps improve my programming skills for this project.

3.1.2 Internet broadband

The access to internet is very important as most researches could be done by a “click” of

the mouse. It is especially essential as many datasheets are available in the World Wide

Web for information. The Broadband connection will help speed up the downloading of

the information we need.

3.2 Project Task and Schedule

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There were slight changes between the planned schedule and the actual dates. Such

changes were unavoidable; as there are many other commitments such as assignments,

exams, work and unforeseen circumstances. From the tight schedule, project plan from

initial report is presented, and discrepancy in the schedules will be discussed.

Project tasks are divided into nine sections.

1. Project Consideration and Selection Process

2. Literature Search

3. Preparing for Initial Report (TMA01) @ Project Proposal

4. Digital image processing using MATLAB

5. Statistical Analysis

6. Texture features extraction and normalization

7. Classification

8. Preparing for Final Report (Thesis)

9. Preparing for oral Presentation

In Task 1, we are allowed to consider the project and need to select the

interested project. It takes us about 7 days to choose. And the project committee

makes allocation for proposed project. It takes about 8 days to approve.

Since Literature research is one of the most important steps for understanding of

the project, 31 days are used for Task 2. It is mainly focus on library reference

books and online IEEE journals.

Preparation of initial report partially depends on Task 2. Task 2 and 3 were

carried out at the same time and additional 7 days were used to complete the

proposal.

Task 4 scheduled for 4 weeks to complete. This task is to study on digital image

processing and how to write MATLAB code as well as the algorithms.

We set to complete the project on 1st

Oct 2010. The duration for Task 5, 6 and 7

is 150 days. These tasks are very important and main tasks for the project. In

these tasks, focus on texture features extraction and normalization as well as

classification.

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For task 8, preparation for final report is 84 days as it is portrayal of our whole

project work and 40% of capstone project score is carried by this task. It will

start from 1st

Aug 11 and end at submission date 14th

Nov 11 and it will also be

carried out concurrently with task 7.

For task 9, preparation for oral presentation will start 3 weeks before finish

writing of the report. There are 21 days available for this task.

Table 3.1 explained detailed project plan.

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Computer Based Diagnosis of Glaucoma Using PCA: A Comparative Study

Tasks Description

Start Date

End Date Duration

( Days )

Resources

1. Project Consideration and Selection Process 2-Jan-11 16-Jan-11 15

Library

Resources

(Reference

Books and

online

IEEE

journals )

MATLAB

Software

Web

Resources

(IEEE

Journals,

Past

Thesis,

NI

website,

Related

reference

Books)

School

Lab

Facility &

Personal

computer

1.1 Project consideration 2-Jan-11 8-Jan-11 7

1.2 Selection of the project 9-Jan-11 16-Jan-11 8

2. Literature Search 17-Jan-11 16-Feb-11 31

2.1 Research on IEEE online journals , relevant

reference books and former student thesis report

17-Jan-11

30-Jan-11

14

2.2 Analyze and study relevant books and journals 31-Jan-11 16-Feb-11 17

3. Preparation of initial report (TMA01) 17-Feb-11 7-Mar-11 20

4. Research on project components 9-Mar-11 8-Apr-11 29

4.1 An introduction to image processing using MATLAB 9-Mar-11 20-Mar-11 12

4.2 Extraction and compiling of MATLAB codes 21-Mar-11 8-Apr-11 17

5. Digital image processing using MATLAB 9-Apr-11 2-May-11 26

5.1 Familiarization with MATLAB codes 7-Apr-11 15-Apr-11 9

5.2 Extraction and collecting texture features 16-Apr-11 24-Apr-11 9

5.3 Overview of texture features 25-Apr-11 2-May-11 8

6. Extraction features using PCA 3-May-11 19-Jun-11 48

6.1 Classification with various data mining techniques 3-May-11 25-May-11 23

6.2 Statistical Analysis 26-May-11 12-Jun-11 18

6.3 Glaucoma integrated index 13-Jun-11 19-Jun-11 7

7. Classification result and comparison 20-Jun-11 31-Aug-11 72

8. Preparation for final report 1-Sep-11 31-Oct-11 61

8.1 Writing skeleton of final report 1-Sep-11 7-Sep-11 8

8.2 Writing Literature search 8-Sep-11 15-Sep-11 8

8.3 Writing Introduction of report 16-Sep-11 22-Sep-11 8

8.4 Writing Main body of report 23-Sep-11 14-Oct-11 22

8.5 Writing conclusion and further study 15-Oct-11 20-Oct-11 5

8.6 Finalizing and amendments of report 21-Oct-11 31-Oct-11 10

9. Preparation for oral presentation 1-Nov-10 2-Dec-11 32

9.1 Review the whole project for presentation 1-Nov-11 27-Nov-11 27

9.2 Prepare poster for presentation

28-Nov-11

2-Dec-11

5

Table 3.1: Detail Project Plan

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CHAPTER 4: DESIGN AND ALGORITHM

4.1 Project Approach and Method

In this project, glaucoma is diagnosed using digital fundus images. The texture features

were extracted from the digital fundus images using Principal Component Analysis

(PCA) method. The 60 fundus images (30 normal images and 30 glaucoma images with

an age group of 20 to 70 years) were collected from the Kasturba Medical College,

Manipal, India. Images are stored in Bitmap format with an image size of 560x720

pixels. A fundus camera is designed to take pictures of the inner surface of the eye. A

fundus camera is one of the most popular devices used for Ophthalmoscopy and used by

doctor to diagnose eye diseased as well as to monitor their progression. Figure 4.1 (a)

shows the fundus camera (which consists of a microscope attached with a camera and

light source) and Figure 1 (b) shows fundus image.

Figure 4.1: (a) Fundus Camera and (b) Fundus Image

Figure 4.2(a) shows normal eye digital fundus image and Figure 2(b) shows glaucoma

eye digital fundus image with a resolution of 560 x 720.

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Figure 4.2: (a) Normal Eye Fundus Image and (b) Glaucomatous Eye Fundus Image

The method we had employed for the analysis of the images involves the image

processing procedure described details in the following section. The block diagram of

the proposed system for the detection of glaucoma is shown in Figure 1.1. The first step

is pre-processing of image data to remove the non-uniform background which may be

due to non-uniform illumination or variation in the pigment color of eye. Adaptive

histogram equalization operation was performed to solve this problem. This technique

computes several histograms, each corresponding to a distinct section of the image, and

uses them to redistribute the lightness values of the image. Subsequently, these images

were converted to grayscale.

As a second step, various groups of texture features were extracted from each digital

fundus image. The two groups of normal and glaucoma features were normalized. The

p-value is calculated from the normalized data by using Student’s T-Test which used to

assess whether the means of two groups are statistically different from each other. This

p-value is calculated for each feature and p-values with below 0.05 are regarded as

statistically significant. A lower p-value indicates that the two groups are statistically

different. The ability to assess the difference between the data groups is important for

this study, because we want to assess the capability of the extracted features to

discriminate between neuropathy and normal data.

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PCA method is used to extract features from the images and fed to various data mining

techniques namely: K-Nearest Neighbor (K_NN), Naïve Bayes Classifier (NBC),

Decision Tree (DeTr) and Probabilistic Neural Network (PNN) for comparison.

4.2 Different Texture Features Study

Texture features derived from Gray Level Co-occurrence Matrix (GLCM), Entropy,

Energy, Homogeneity, Contrast, Symmetry, Correlation, Moment, Mean, Run-length

matrix such as Short run emphasis, Long run emphasis, Run Percentage, Gray level

non-uniformity and Run length non-uniformity are explained earlier. In this section,

brief explanations of Fractal Dimension, Local Binary Patterns, Laws‟ Texture Energy

and Fuzzy Gray Level Co-occurrence Matrix are described as follows.

4.2.1 Fractal Dimension

The concept of fractal was first introduced by Benoit Mandelbrot in 1975. In his

opinion, fractals objects have three important properties, (1) Self similarity (2) Iterative

formation and (3) Fractional dimension. There are many instances of fractals in nature

such as Contour set, Sierpinski triangle, Koch snow, Julia set, Fern fractal, etc. There

are many specific definitions of fractal dimension. The most important theoretical

fractal dimensions are:

1) Hausdorff dimension

2) Packing dimension

3) Rényi dimensions

4) Box-Counting dimension

5) Correlation dimension

Fractal dimension is estimated as the exponent of power law. Fractal analysis can be

seen in various application areas and most common interest is to determine the fractal

dimension of the concerned objects. Fractal dimension is a real number used to

characterize the geometric complexity of A. A bounded set A in Euclidean n-space is

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log(N

)

log(N

)

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self-similar if A is the union of Nr distinct (non-overlapping) copies of itself scaled up or

down by a factor of r. The fractal dimension D of A is given by relation (1).

1 Nr r (4.1)

logN D r

1 (4.2)

log r

14 14

12 12

10 10

8 8

6 6

4 4

2 2

0

1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6

log(1/r)

0 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6

log(1/r)

Figure 4.3: (a) Normal FD and (b) Glaucoma FD

Fractal Dimension (FD) will be extracted using MATLAB program fractal.m which

generated fractal data associated with the graph as shown above. MATLAB codes are

listed in appendix N.

4.2.2 Local Binary Patterns

Local Binary Patterns (LBP) is a very powerful texture feature which discrete

occurrence histogram of the “Uniform” patterns computed over an image or a region of

image. It‟s defined as a gray-scale invariant texture measure, effectively combines

structural and statistical approaches by computing the occurrence histogram. The local

binary pattern detects microstructures (e.g., edges, lines, spots, flat areas) whose

underlying distribution is estimated by the histogram. The LBP operator can be seen in

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most of the real world applications because of its simple computation and possible to

analyze images in real time as well as texture segmentation to face recognition. The

image texture is characterized by two orthogonal properties: spatial structure (pattern)

and contrast (the „amount‟ of local image texture) where spatial pattern is affected by

rotation and contrast is affected by gray scale.

The LBP operator provides a unified approach to the traditionally divergent statistical

and structural model of texture analysis. In this paper, the LBP feature vector is

calculated as follows. Arbitrary circular neighborhoods around the uniform pattern pixel

„P‟ points are chosen on the circumference of the circle with radius „R‟. On the circular

neighborhood, the grayscale intensity values at points that do not coincide exactly with

pixel locations are estimated by interpolation. Figure 4.4 shows circularly symmetric

neighbor sets for different values of P and R. Figure 4.5 depicts square neighborhood

and circular neighborhood.

Figure 4.4: Circularly symmetric neighbor sets for different P and R

Figure 4.5: Square neighborhood and Circular neighborhood

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Let gc be the intensity of the center pixel and gp, p=0,…., P-1, be that of the P points.

These P points are converted to a circular bit-stream of 0s and 1s according to whether

the intensity of the pixel is less than or greater than the intensity of the center pixel.

From each pixel the uniform pixels are used for further computation of texture

descriptor and the non-uniform patterns are assigned to single bin. These “uniform”

fundamental patterns have a uniform circular structure that contains very few spatial

transitions U (number of spatial bitwise 0/1 transitions). The texture primitives for

microstructures detected by the uniform patterns of LBP such as bright spot (U=0), flat

area or dark spot (U=8), and edges of varying positive and negative curvature (U=1-7).

Figure 4.1.3 shows uniform and non-uniform patterns.

Figure 4.6: Uniform and Non-uniform patterns

Therefore, a rotation invariant measure called LBPP,R using uniformity measure U is

calculated based on the number of transitions in the neighborhood pattern. Only patterns

with U ≤ 2 are assigned the LBP code as:

LBPP,R

P1

s( g g ) P0

U ( x) 2

(4.3)

P 1 otherwise

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where

1

s( x) 0

x 0

x 0

The center pixel is labeled as uniform if the number of bit-transitions in the circular bit-

stream is less than or equal to 2. A look up table is generally used to compute the bit-

transitions to reduce computational complexity. Multi-scale analysis is done by

combining the information provided by N-operators and summing up operator-wise

similarity scores into an aggregate similarity score. The LBP operator chooses the

circles with various degrees around the center pixels and constructing separate LBP

image for each scale. In this project, energy and entropy of the LBP images are

constructed over different scales (R=1, 2, and 3 with the corresponding pixel count P

being 8, 16 and 24 respectively) were used as feature descriptors. A total of nine LBP

based features were extracted from the studied image. Table 4.2 shows the nine LBP

based features for normal and glaucoma images with their respective p-value.

Features Normal Glaucoma P-Value

LBP1 2.27 ± 0.363 1.93 ± 0.288 < 0.0002

LBP2 0.321 ± 0.131 0.183 ± 8.555E-02 < 0.0001

LBP3 0.401 ± 7.747E-02 0.326 ± 5.124E-02 < 0.0001

LBP4 2.86 ± 0.592 2.45 ± 0.390 < 0.0026

LBP5 0.539 ± 0.148 0.394 ± 9.297E-02 < 0.0001

LBP6 0.475 ± 1.087E-02 0.478 ± 8.898E-03 < 0.26

LBP7 3.53 ± 2.09 3.01 ± 0.503 < 0.0066

LBP8 0.604 ± 0.124 0.481 ± 7.595E-02 < 0.0001

LBP9 0.473 ± 1.931E-02 0.494 ± 8.663E-03 < 0.0001

Table 4.1: LBP features for normal and glaucoma images with p-vale

Local Binary Patterns (LBP) will be extracted using MATLAB program lbpana.m

which calls another MATLAB function programs lbp.m. These MATLAB codes are

listed in appendix O.

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4.2.3 Laws’ Texture Energy

Laws‟ texture energy measure is computed by applying small convolution kernels to a

digital image and then performing a non-linear window operation. It‟s another approach

of detecting various types of textures by using local masks. Laws marks represented

image features without referring to the frequency domain. Texture energy measure is

based on texture energy transforms applied to the image to estimate the energy within

the pass region of filters. The texture description uses such as:

1) Average gray level

2) Edges

3) Spots

All the masks were derived from one dimensional (1-D) vectors of three pixels length:

1) L3 = [1 2 1] averaging

2) E3 = [-1 0 1] first difference-edges

3) S3 = [-1 2 -1] second difference-spots

Nine 2-D masks of size 3 x 3 can be generated by convolving any vertical 1-D vector

with a horizontal one as shown in Equation (4.4).

1 1 0 1 2 * 1 0

E 3 1 2 0 2 (4.4)

1 L3

1 0 1 L3 E 3

To extract texture information from an image I(i,j), the image was first convoluted with

each 2-D mask. To filter the image I(i,j),with E3S3, the result is a Texture Image

(TIE3E3) as shown in Equation (4.5).

TIE3E3 = I(i,j)*E3S3 (4.5)

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According to Laws‟ suggestion, all the convolution kernels used is zero mean with an

exception of the L3L3 kernel. And this image (TIL3L3) was used to normalized the

contrast of the remaining texture images TI(i,j) with the eight zero-sum masks

numbered 1 to 8. Equation (4.6) shows the normalization of Texture Image.

NormalizeTI TI

(i , j )

m a sk

(4.6) mask TI

(i , j )L 3 L 3

The outputs (TI) from Laws‟ masks were passed to Texture Energy Measurement

(TEM) filters (4.7). These consisted of moving non-linear window average of absolute

values. The images were filtered using the eight masks and energies were computed.

The computed features were chosen to quantify the changes in the levels, edges, and

spots in the studied image. Eight LTE based features were extracted from the image by

using above eight masks. Table 4.3: shows all the eight LTE features of normal and

glaucoma with their respective p-value.

3 3

TEM (i , j ) TI(iu , j v )

u3v3

(4.7)


LTE1

0.341 ± 0.220

0.740 ± 0.279

< 0.0001

LTE2

0.346 ± 0.306

0.692 ± 0.299

< 0.0001

LTE3

0.298 ± 0.285

0.726 ± 0.228

< 0.0001

LTE4

0.241 ± 0.269

0.731 ± 0.297

< 0.0001

LTE5

0.164 ± 0.255

0.682 ± 0.291

< 0.0001

LTE6

0.294 ± 0.407

0.702 ± 0.277

< 0.0001

LTE7

0.223 ± 0.244

0.689 ± 0.280

< 0.0001

LTE8 0.173 ± 0.251 0.675 ± 0.278 < 0.0001

Table 4.2: LTE features of normal and glaucoma images with p-vale

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Laws‟ Texture Energy (LTE) will be extracted using MATLAB program MAIN.m

which calls four other MATLAB function programs lawsanalysis.m, lawsmask.m,

lawsfilter.m and lawsimg.m. These MATLAB codes are listed in appendix P.

4.2.4 Fuzzy Gray Level Co-occurrence Matrix

Gray Level Co-occurrence Matrix (GLCM) is a matrix that represents second-order

texture moments, and it‟s described the frequency of one gray level appearing in a

specified spatial linear relationship with another gray level within the neighborhood of

interest. The Fuzzy Gray Level Co-occurrence Matrix (FGLCM) of an image I of size L

x L is given by:

Fd m, n fmn

LxL

(4.8)

where fmn corresponds to the frequency of occurrence of a gray value „around m‟ and

different pixel with gray value „around n‟, which are the relative distance between the

pixel pair d measure in relative orientation θ. It is represented as: F = f (I,d,θ).

In this paper, θ is quantized in four direction (0º, 45º, 90º, 135º) for a distance d = 20

with a rotational invariant co-occurrence matrix „F‟, to find the texture feature value.

Equation (4.9) and (4.10) depict FGLCM based features.

Energy: E fuzzy Fd m, n

(4.9) m n

Entropy: H fuzzy Fd m, n. ln Fd m, nm n

(4.10)

The homogeneity feature is used to measure the similarity between two pixels that are

apart, (Δm,Δn) and the contrast feature is used to captured the local variation between

those two pixels. The degree of disorder and the denseness in an image are measured by

energy and entropy features. The entropy feature will have a maximum value when all

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elements of the co-occurrence matrix are the same. FGLCM features of normal and

glaucoma images and their respective p-value are shown in Table 4.4.

Features Normal Glaucoma P-Value Homogeneity 0.1965±0.01860 0.2239±0.0336 < 0.0006

Energy 7.354E+03 ±5.13E+02 8.402E+03±5.29E+02 < 0.0005

Entropy 3.6614 ±.056417 3.543 ±0.046 < 0.0002 Contrast 15.2557 ±0.5096 13.8617 ±0.5728 < 0.0014

Symmetry 1.000 ± 3.608E-04 1.000 ± 3.191E-04 < 0.079 Correlation 8.704E-03 ± 6.622E-04 8.051E-03 ± 4.727E-04 < 0.0001

Moment1 -2.621E-02 ±1.170E-02 -1.81E-02 ±2.940E-02 < 0.016 Moment2 1.2162E+03 ±1.054E+03 4.63842E+02 ±1.156E+02 < 0.0014

Moment3 -1.209E+03 ± 4.04E+03 -4.2751E+02 ±1.528E+03 < 0.016 Moment4 7.372E+05 ±6.65E+05 1.253150E+06 ±1.211E+06 < 0.0014

Angular2ndMoment 1.238E+10 ± 9.827E+08 1.324E+10 ± 2.335E+09 < 0.067 Contrast 1.943E+06 ± 1.399E+06 9.061E+05 ± 1.001E+06 < 0.0017

Mean 8.812E+03 ± 5.574E+03 4.713E+03 ± 3.988E+03 < 0.0018 Entropy -1.808E+03 ± 98.3 -1.848E+03 ± 228. < 0.39

ShortRunEmphasis 0.855 ± 1.931E-02 0.834 ± 2.857E-02 < 0.0012

LongRunEmphasis 6.21 ± 5.17 10.2 ± 4.38 < 0.0019 RunPer 2.19 ± 0.210 2.06 ± 0.169 < 0.0091

GrayLvlNonUni 4.0523E+04 ±5.960E+03 3.6804E+04 ±4.429E+03 < 0.0011

RunLengthNonUni 6.8813E+02 ±53.99 849 ±183 < 0.13

Table 4.3: FGLCM features of normal and glaucoma images with p-vale

Fuzzy Gray Level Co-occurrence Matrix (FGLCM) will be extracted using MATLAB

program Main.m which calls six other MATLAB function programs fuzzycoocc.m,

jtxtAnalyCCMFea.m, jfDStats.m, jrLengthMat.m, jrRLength.m and jcoMatrix.m.

These MATLAB codes are listed in appendix Q.

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CHAPTER 5: CLASSIFICATIONS AND RESULTS

5.1 Principal Component Analysis (PCA)

In this project, the normalizing features were extracted by using Principal Component

Analysis (PCA) method and the features were fed to the PNN classifier. Principal

Component Analysis (PCA) is a mathematical procedure that transforms a number of

possible correlated variables into a smaller number of uncorrelated variables called

principal components. PCA is a powerful tool for analyzing data because it is a simple,

non-parametric method of extracting relevant information from confusing data sets. The

other main advantage of PCA is when identifying patterns in the compress data (or by

reducing the number of dimension) the information loss is very less. The number of

principal components is less than or equal to the number of original variables.

In this work, the 33 dimension of data set were reduced to 12 number of dimension. For

PCA to work properly, the mean value is subtracted from each of the data dimensions

and its produces a data set whose mean is zero. The eigenvalues and eigenvectors were

calculated from the covariance matrix. These are important as it gives useful

information about the data. After forming a feature vector, multiplying it with the

original data set by taking the transpose of the feature vector.

FinalData RowFeatureVector RowDataAdjust

In order to get the original data back, we used the equation shows below.

RowDataAdjust RowFeatureVector 1 FinalData

RowDataAdjust RowFeatureVector T FinalData

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RowOriginalData (RowFeatureVector T FinalData ) OriginalMean

The equation still gives the correct transform without present of all the eigenvectors.

Principal Component Analysis (PCA) will be extracted using MATLAB program

Test1.m which generated 12 selected PCA components. MATLAB codes are listed in

appendix R.

5.2 Classifier Used

A Probabilistic Neural Network (PNN) is an implementation of a statistical algorithm

called kernel (Kernels are also called “Parzen Windows”) discriminant analysis in

which the operation are organized into a multilayered feed forward network with four

layers:

Input Layer

Pattern Layer

Summation Layer

Output Layer

Figure 5.2: shows the architecture of a typical PNN. PNN architecture is composed of

several sub-networks or neurons organized in successive layers and each of which a

normalized RBF network is also known as “kernels”. These “Parzen Windows” are

usually the probability density functions such as Gaussian function. The input nodes are

the set of measurements and its does not perform any computation. It‟s simply

distributes the input to the neurons in the pattern layer. The hidden-to-output weights

are usually 1 or 0. The neuron of the pattern layer computes its output after receiving a

pattern from the input layer. The dimension of the pattern vector is the smoothing

parameter and is the neuron vector. The summation layer neurons compute the

maximum likelihood of pattern being classified into by summarizing and averaging the

output of all neurons that belong to the same class. The prior probabilities for each class

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are the same, and the losses figures are added to the PDF as a weight. The decision layer

unit classifies the pattern in accordance with the Bayes‟s decision rule based on the

output of all the summation layer neurons.

Figure 5.1: Architecture of PNN

Data enters at the inputs and passes through the layer of hidden layers, where the actual

information is processed and the result is available at the output layer. Usually feed-

forward architecture is used, where there is no feedback between the layers. The

supervised learning algorithm was used for training the neural network. In the case,

initially the system weights are randomly chosen and then slowly modified during the

training in order to get the desired outputs. The difference between the actual output and

desired output is calculated for each input at every iteration. These errors are used to

change the weights proportionately. This process continues until the preset mean square

error is reached (0.001 in this work). This algorithm of reducing the errors in order to

achieve the correct class by incrementing weights is known as back-propagation.

5.3 Results

In this study, 60 fundus images which consist of 30 Normal fundus images and 30

Glaucoma fundus images with an age group of 20 to 70 years were used. Out of 33

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features retrieved with the processing technique discussed above, 12 features were

computed by using Principal Component Analysis (PCA). A student T-Test was

conducted on these two groups whether the mean value of each texture feature was

significantly different between the two classes. It was found that all these 12 features

tested were clinically significant which p-value is less than 0.05. The mean and standard

deviation values of computed features are shown in Table 5.1. From the table we can

see that p-value of all PCA features are clinically significant which means lover p-value.

The p-value is the probability of rejecting the null hypothesis assuming that the null

hypothesis is true.


PCA1 0.400 ± 7.311E-02 0.320 ± 4.731E-02 < 0.0001

PCA2 0.388 ± 7.855E-02 0.317 ± 4.760E-02 < 0.0001

PCA3 0.382 ± 7.566E-02 0.312 ± 4.835E-02 < 0.0001

PCA4 0.373 ± 7.315E-02 0.306 ± 4.708E-02 < 0.0001

PCA5 0.357 ± 6.772E-02 0.290 ± 4.864E-02 < 0.0001

PCA6 0.345 ± 6.820E-02 0.257 ± 5.815E-02 < 0.0001

PCA7 0.334 ± 6.982E-02 0.237 ± 6.198E-02 < 0.0001

PCA8 0.323 ± 6.901E-02 0.223 ± 5.513E-02 < 0.0001

PCA9 0.308 ± 6.192E-02 0.212 ± 5.441E-02 < 0.0001

PCA10 0.278 ± 4.804E-02 0.193 ± 5.278E-02 < 0.0001

PCA11 0.265 ± 4.561E-02 0.180 ± 5.825E-02 < 0.0001 PCA12 0.253 ± 4.543E-02 0.157 ± 6.248E-02 < 0.0001

Table 5.1: 12 Features of normal and glaucoma PCA and their p-value

In order to test the performance of classifier, we choose the three-fold stratified cross

validation method. The advantage of this method is that all observations are used for

both training and testing (validation), and each observation is exactly used for validation

once. Figure 5.2: shows the three-fold stratified cross validation. Two parts of the data

set were used for training and the remaining one set of data was used to test the

performance (i.e. 21 images were used for training and 9 images were used for testing

each time). This process was repeated three times using different sessions of the test

data each time.

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TRAIN

MODEL

TEST

RESULT

Figure 5.2: Procedure of three-fold stratified cross validation

After processing three different test, TP (true positive), TN (true negative), FP (false

positive), FN (false negative), accuracy, sensitivity, specificity and positive predictive

accuracy were obtained by taking the average of the values computed in the three

iterations. Table 5.2: shows the PNN classification result.

TN FN TP FP ACC SENSI SPECI PPV

9.0000 2.0000 7.0000 0.0000 76.1905 100.0000 81.8182 100.0000 PNN 9.0000 0.0000 9.0000 0.0000 85.7143 100.0000 100.0000 100.0000

8.0000 0.0000 9.0000 1.0000 80.9524 90.0000 100.0000 90.0000 AVERAGE 80.9524 96.6667 93.9394 96.6667

Table 5.2: PNN classification result

True Negative (TN) is the number of normal images classified as normal images.

False Negative (FN) is the number of glaucomatous images classified as normal.

True Positive (TP) is the number of glaucoma images classified as glaucoma.

False Positive (FP) is the number of normal images classified as glaucomatous.

In our work, PNN able to classified successfully with accuracy rate of 80.9%, sensitivity

96.7%, specificity 93.9% and positive predictive accuracy (PPV) 96.7% which is

clinically significant.

Sensitivity is the probability of abnormal class is classified as abnormal.

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Sensitivity TP

*100%

(5.1) TP FN

Specificity is defined as the probability of normal class is identified as normal.

Specificit y TN

*100%

(5.2) TN FP

The Positive Predictive Accuracy (PPV) shows the accuracy of detecting the normal and

abnormal cases.

PPV TP

*100%

(5.3) TP FP

PNN classifier has orders of magnitude faster than back-propagation and able to

converge to an optimal classifier as the size of the training set increases. The most

important characteristic is that training samples can be added or removed without

extensive retraining.

Probabilistic Neural Network (PNN) will be extracted using MATLAB program

aatrain.m which trains the data and another MATLAB program aatest.m is used to test

the input data. MATLAB codes are listed in appendix S.

5.4 Glaucoma Integrated Index

Integrated index provide better way of tracking how much each of the 12 features varies

from their respective normal values to make a diagnosis. It is more useful to combine

the features into an integrated index, in such a way that the value of this index is

significantly different between normal and glaucoma subjects. Integrated index was

used for biomedical applications such as Ghista 2004, 2009a, 2009b and Acharya et al.

2011a, 2011b respectively. We came up with an integrated index by combining the

features to get the integrated index value is distinctly different for normal and glaucoma

subjects. The proposed mathematical formulation of GII is:

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200 PCA12 2 PCA1 GII (5.4)

100

Although all the parameters in Table 5.1 were clinically significant, the range of PCA1

and PCA12 for normal and glaucoma classes was wide compared to the rest of the

features. Hence, we have selected PCA1 and PCA12 in the GII formula. And also, the

combination of PCA1 and PCA12 yielded the best separation of the two classes

compared to the other combinations using PCA2 to PCA11. The computed GII values

for normal and glaucoma subjects are shown in Table 5.3.

Index Normal Glaucoma P-Value

GII 4.79 ± 0.158 4.30 ± 0.117 < 0.0001

Table 5.3: The GII values for normal and glaucoma subjects

Figure 5.3: shows the distribution plot of this integrated index for normal and glaucoma

subjects. The distinctive difference of this index between the two classes indicates that it

can be effectively employed to differentiate and diagnose normal and glaucoma

subjects.

Figure 5.3: The distribution plot of the GII for normal and glaucoma

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CHAPTER 6: DISCUSSION, CONCLUSION AND

RECOMMENDATION

6.1 Discussion

Principal Component Analysis (PCA) is proposes method for automatic glaucoma

identification using texture features extracted from fundus images. The Student T-Test

shown that our features are clinically significant to detect the glaucoma. There are

several methods of treatment available to impede progression glaucoma. For this reason,

it is important to diagnose glaucoma as early as possible to minimize the damage to the

optic nerve.

In the past, Fuzzy sets were used to provide for medical diagnosis and the six fuzzy

classification algorithms performed less than 76% in identifying the correct class. The

high diagnostic performance of ANN based on refined input visual field data achieved a

sensitivity of 93% at a specificity level of 94% with an area under the receiver operating

characteristic curve of 0.984. Glaucoma Hemifield test attained a sensitivity of 92% at

91% specificity. Heidelberg retina tomography (HRT) was used to differentiate between

glaucoma and non-glaucoma eye using neural network. The ROC (receiver operating

characteristics) curves for SVM (support vector machine) was 0.938 and SVM Gaussian

was 0.945. MLP (multi-layer perceptron) and the current LDF (linear discriminant

function) were 0.941 and 0.906 respectively. For the best previously proposed LDF was

0.890.

In our work, we have extracted 12 texture features using image processing techniques

and PCA methods. The significant features were selected (for lower p-values) using the

Student T-Test. The capability of the features for good diagnosis was studied using PNN

classifiers. The low p-value obtained using T-Test and the high accuracy values

obtained by using these features in classifiers indicate the usefulness of these features.

However, for a medical specialist, the meaning of these features might not be apparent.

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In addition, to improve the accuracy of diagnosis based on these features, due to some

features have larger values for glaucoma subjects (than for normal subjects) and other

features have smaller values for glaucoma subjects (than for normal subjects). Hence,

for more comprehensibility and transparency, we have developed an integrated index

using these features. The Glaucoma Integrated Index has demonstrated good

discriminative power for the normal subjects and glaucoma subjects.

The interpretation using this index is simple – a GII value below 4.417 indicates the

presence of glaucoma. This simplicity, transparency and objectivity of the GII make it

user-friendly and enable it to become a valuable addition to Glaucoma analysis software

and hardware. The cost of installing the GII feature into the software of the existing

diagnosis systems is much lower, because the calculation of the index value involves

only digital signal processing tools and this type of processing is cheap and readily

available.

The results show superior to some of those existing methods due to the higher

percentage of correct classification. However, we can improve the accuracy by using

more parameters or by increasing the number of training and testing images. The

percentage of correct classification also depends on the environmental lighting

conditions. This method can be used as an adjunct tool for the physicians to cross check

their diagnosis.

6.2 Conclusion

A computer based system for detection of glaucoma abnormal eyes through fundus

images is developed algorithm using image processing techniques, Principal Component

Analysis (PCA) and Probabilistic Neural Network classifier. The features are computed

automatically and this gives us a high degree of accuracy. These features were tested by

using Student T-Test, which showed that all 12 PCA features are clinically significant.

The system we propose can identify the presence of glaucoma to the accuracy of 80.9%.

Another proposed method of this work is to combine the features into an integrated

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index in such a way that its value is distinctly different for normal and glaucoma

subjects. Moreover, the result of the system can further be improved by taking more

diverse images. This system can be used as an adjunct tool by the physicians to cross

check their diagnosis. However, early detection is important to prevent the progression

of the disease.

6.3 Recommendation

This project carried out by extracting 33 texture features form digital fundus images and

12 principal components were computed with Principal Component Analysis (PCA)

method. These features were tested by means of t-test, which showed that the 12 PCA

features are all clinically significant. These 12 features were fed into the PNN classifier

to generate the classification result. The development of Glaucoma Integrated Index

demonstrated good discriminative power for normal subjects and glaucoma subjects.

Our system produces encouraging result but it can further be improved by taking more

diverse images and evaluating better features.

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PART II

CRITICAL REVIEW AND REFLECTION

Throughout this work, I had spent huge amount of time on Literature research on the

MATLAB programming such as digital image processing, texture features extraction

codes, PNN classification and so on. Among all the tasks, project management and time

management skills are the most important part over the entire project work. From the

initial start of the project, I knew it would not be easy because of tight schedule and

limited knowledge about MATLAB. However upon completion of the project, I was

able to better understand my strengths and weakness.

A lot of research and reading was done over the reference books form library and IEEE

and other published journals to explore more about the project. Understanding all the

texture features is not possible without my supervisor guidance and explanation. Skills

like project management and information researches are greatly enhanced over the

period of the entire project. I would say Project management skill is the most important

part in making the project a successful one.

Technical report writing is one of the weak points that I need to overcome. The “Project

Report Writing and Poster Design Briefing” provided by UniSIM was really helpful.

Report writing skills also gradually improved throughout the progress. Since, MATLAB

is the backbone of this project; I have to understand the concept of how the features

were extracted from images and their algorithms using MATLAB codes. I gained a lot

of experiences after completion of this project; such as problem solving skill, MATLAB

programming skill, analytical skill, project management skill and last but not least

technical report writing skill.

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http://www.mathworks.com/matlabcentral/fileexchange/17482-gray-level-run-length-matrix-toolbox

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http://www.singapore-glaucoma.org/index.php?option=com_content&view=article&id=112:savh-new-referrals-to-lvc-27-of-cases-are-glaucoma&catid=3:newsflash&Itemid=37



http://grass.fbk.eu/gdp/html_grass64/r.texture.html

http://en.wikipedia.org/wiki/Kurtosis

http://en.wikipedia.org/wiki/Fractal_dimension

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http://www.ccs3.lanl.gov/~kelly/ZTRANSITION/notebook/laws.shtml

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http://herselfsai.com/2007/03/probabilistic-neural-networks.html


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APPENDIXES

Appendix A: Moments and FD features with their normalized value for normal images

Moment1 Moment2 Moment3 Moment4 FD 39.5686 0.486781 5.45E+03 3.79E-01 1.42E+05 1.35E-01 9.53E+06 7.79E-02 2.1416 0.988051 40.8667 0.50275 4.12E+03 2.87E-01 1.39E+05 1.32E-01 8.42E+06 6.88E-02 2.1103 0.97361 39.8039 0.489675 3.97E+03 2.76E-01 1.21E+05 1.15E-01 8.74E+06 7.14E-02 2.103 0.970242 40.2196 0.494789 3.60E+03 2.51E-01 1.38E+05 1.31E-01 8.33E+06 6.81E-02 2.1215 0.978777 38.8314 0.477711 4.02E+03 2.80E-01 1.28E+05 1.22E-01 8.40E+06 6.87E-02 2.1377 0.986251 81.2863 1 5.59E+03 3.89E-01 2.77E+05 2.64E-01 1.64E+07 1.34E-01 2.0891 0.963829 79.5176 0.978241 1.02E+04 7.12E-01 1.05E+06 1.00E+00 1.22E+08 1.00E+00 2.1005 0.969089 39.6157 0.48736 3.10E+03 2.16E-01 1.32E+05 1.26E-01 8.20E+06 6.71E-02 2.0926 0.965444 5.3412 0.065708 1.26E+03 8.75E-02 5.21E+03 4.96E-03 4.83E+05 3.95E-03 2.0694 0.95474 40.251 0.495176 7.38E+03 5.14E-01 2.58E+05 2.46E-01 2.95E+07 2.41E-01 2.1258 0.980761 10.749 0.132236 1.49E+03 1.04E-01 8.76E+03 8.33E-03 5.23E+05 4.28E-03 2.075 0.957324 14.8431 0.182603 2.45E+03 1.70E-01 2.21E+04 2.10E-02 2.54E+06 2.07E-02 2.0848 0.961845 13.698 0.168515 1.16E+03 8.04E-02 49.9725 4.76E-05 3.95E+05 3.23E-03 2.0866 0.962676 7.9882 0.098272 1.18E+03 8.20E-02 5.62E+03 5.35E-03 2.57E+05 2.10E-03 2.0973 0.967612 22.9373 0.282179 3.68E+03 2.56E-01 6.01E+03 5.72E-03 2.03E+06 1.66E-02 2.1123 0.974533 14.8941 0.18323 2.60E+03 1.81E-01 2.41E+04 2.29E-02 1.24E+06 1.02E-02 2.1054 0.971349 26.7686 0.329313 4.51E+03 3.14E-01 1.61E+04 1.53E-02 4.59E+06 3.76E-02 2.1258 0.980761 40.251 0.495176 7.38E+03 5.14E-01 2.58E+05 2.46E-01 2.95E+07 2.41E-01 2.1258 0.980761 41.8196 0.514473 6.91E+03 4.81E-01 1.27E+05 1.21E-01 1.52E+07 1.24E-01 2.1246 0.980208 14.3137 0.17609 6.74E+03 4.69E-01 2.11E+03 2.01E-03 2.48E+07 2.03E-01 2.1073 0.972226 65.8588 0.810208 7.18E+03 5.00E-01 3.05E+05 2.90E-01 2.47E+07 2.02E-01 2.1175 0.976932 66.3333 0.816045 7.15E+03 4.97E-01 3.06E+05 2.91E-01 2.48E+07 2.02E-01 2.1159 0.976194 64.698 0.795927 6.71E+03 4.67E-01 2.96E+05 2.82E-01 2.49E+07 2.03E-01 2.1074 0.972272 67.8078 0.834185 7.01E+03 4.88E-01 3.25E+05 3.09E-01 2.73E+07 2.23E-01 2.0982 0.968028 51.1216 0.628908 1.04E+04 7.26E-01 1.89E+05 1.80E-01 4.71E+07 3.85E-01 2.1153 0.975917 42.8824 0.527548 1.08E+04 7.52E-01 7.17E+03 6.83E-03 7.03E+07 5.75E-01 2.1675 1 64.5137 0.79366 1.44E+04 1.00E+00 4.81E+05 4.58E-01 1.03E+08 8.41E-01 2.1305 0.98293 67.1804 0.826466 6.98E+03 4.86E-01 2.60E+05 2.47E-01 2.20E+07 1.80E-01 2.1203 0.978224 63.1176 0.776485 5.77E+03 4.02E-01 3.14E+05 2.98E-01 2.43E+07 1.99E-01 2.0913 0.964844 69.051 0.849479 7.20E+03 5.01E-01 2.52E+05 2.40E-01 2.22E+07 1.82E-01 2.1211 0.978593

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Appendix B: Moments and FD features with their normalized value for glaucoma images

Moment1 Moment2 Moment3 Moment4 FD 16.9059 0.535595 3.03E+03 5.20E-01 4.30E+03 7.34E-02 1.08E+06 1.72E-01 2.111 0.991406 18.8784 0.598086 3.37E+03 5.79E-01 5.86E+04 1.00E+00 2.93E+06 4.68E-01 2.1077 0.989856 11.5529 0.366007 2.65E+03 4.55E-01 7.35E+03 1.25E-01 1.49E+06 2.38E-01 2.1001 0.986287 14.3882 0.455832 1.79E+03 3.08E-01 273.7765 4.67E-03 5.78E+05 9.23E-02 2.0876 0.980416 17.6863 0.560319 2.99E+03 5.14E-01 3.88E+04 6.62E-01 5.63E+06 8.99E-01 2.0989 0.985723 18.2392 0.577835 2.94E+03 5.05E-01 4.06E+04 6.93E-01 5.43E+06 8.66E-01 2.0933 0.983093

19.102 0.60517 3.34E+03 5.74E-01 3.85E+04 6.56E-01 5.09E+06 8.13E-01 2.1023 0.98732 24.4745 0.775376 3.51E+03 6.02E-01 2.95E+04 5.04E-01 4.68E+06 7.47E-01 2.1085 0.990232 26.5412 0.840851 4.37E+03 7.51E-01 2.97E+04 5.07E-01 4.53E+06 7.23E-01 2.1225 0.996806 18.8902 0.59846 3.26E+03 5.60E-01 3.79E+04 6.47E-01 5.75E+06 9.18E-01 2.106 0.989057 18.9608 0.600696 2.70E+03 4.63E-01 4.22E+04 7.20E-01 5.45E+06 8.70E-01 2.0844 0.978913 18.7137 0.592868 3.23E+03 5.54E-01 3.91E+04 6.67E-01 5.12E+06 8.17E-01 2.0979 0.985253 27.3216 0.865575 3.65E+03 6.27E-01 3.14E+04 5.36E-01 4.84E+06 7.73E-01 2.1109 0.991359 11.4078 0.36141 2.69E+03 4.62E-01 2.75E+04 4.69E-01 3.71E+06 5.92E-01 2.075 0.974499 27.6039 0.874518 4.29E+03 7.37E-01 2.87E+04 4.90E-01 4.44E+06 7.09E-01 2.1204 0.99582 15.0863 0.477948 3.32E+03 5.70E-01 4.99E+04 8.51E-01 6.26E+06 1.00E+00 2.0879 0.980557 19.4863 0.617345 2.75E+03 4.73E-01 4.34E+04 7.40E-01 5.54E+06 8.84E-01 2.088 0.980604 23.9961 0.760219 3.02E+03 5.19E-01 3.59E+04 6.13E-01 5.82E+06 9.29E-01 2.0981 0.985347 25.3725 0.803825 5.06E+03 8.69E-01 2.19E+04 3.74E-01 5.43E+06 8.67E-01 2.1186 0.994975 18.7529 0.59411 2.67E+03 4.59E-01 2.92E+04 4.98E-01 4.71E+06 7.52E-01 2.0868 0.98004 19.6627 0.622933 2.72E+03 4.66E-01 3.29E+04 5.61E-01 4.83E+06 7.71E-01 2.0874 0.980322 31.2078 0.988693 3.36E+03 5.77E-01 3.64E+04 6.21E-01 4.17E+06 6.66E-01 2.1268 0.998826 23.0157 0.729159 3.03E+03 5.21E-01 4.40E+04 7.51E-01 5.69E+06 9.09E-01 2.0982 0.985394 24.5294 0.777115 3.15E+03 5.42E-01 3.55E+04 6.06E-01 5.78E+06 9.23E-01 2.1024 0.987367 19.7216 0.624799 2.44E+03 4.19E-01 3.22E+04 5.50E-01 3.99E+06 6.36E-01 2.0719 0.973043 26.8824 0.85166 5.82E+03 1.00E+00 2.19E+04 3.74E-01 6.06E+06 9.68E-01 2.1293 1 18.2118 0.576967 2.70E+03 4.64E-01 3.06E+04 5.21E-01 4.74E+06 7.57E-01 2.0873 0.980275 17.7255 0.561561 2.55E+03 4.39E-01 3.29E+04 5.62E-01 4.75E+06 7.58E-01 2.0841 0.978772 22.2431 0.704683 2.88E+03 4.95E-01 3.57E+04 6.10E-01 4.43E+06 7.08E-01 2.0934 0.98314 31.5647 1 3.37E+03 5.78E-01 3.88E+04 6.63E-01 5.21E+06 8.32E-01 2.1216 0.996384

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Appendix C: LBP features with their normalized value for normal and glaucoma images

LBP1 LBP2 LBP3 LBP4 LBP5 LBP7 LBP8 LBP9 2.6657 0.973452 0.3814 0.809938 0.4458 0.95768 3.2572 0.863727 0.6044 0.862812 3.9758 0.811305 0.6406 0.843117 0.4798 0.960945

2.1121 0.77129 0.3964 0.841792 0.4519 0.970784 2.5277 0.670282 0.6233 0.889793 3.0451 0.621386 0.6597 0.868255 0.4738 0.948928 2.0083 0.733384 0.4078 0.866001 0.4562 0.980021 2.3617 0.626263 0.6356 0.907352 2.7941 0.570166 0.6795 0.894314 0.4667 0.934709 2.2905 0.836437 0.4147 0.880654 0.4567 0.981096 2.6947 0.714566 0.6407 0.914632 3.1915 0.65126 0.6784 0.892867 0.4671 0.93551 2.7312 0.997371 0.4044 0.858781 0.4536 0.974436 3.2242 0.854976 0.6258 0.893362 3.8379 0.783165 0.6637 0.873519 0.4724 0.946125 1.795 0.655492 0.4052 0.86048 0.4572 0.98217 2.1141 0.560606 0.6342 0.905353 2.5011 0.510376 0.6709 0.882996 0.4699 0.941118

2.0227 0.738643 0.4098 0.870248 0.4576 0.983029 2.3416 0.620933 0.6389 0.912063 2.727 0.556474 0.678 0.89234 0.4672 0.93571 1.9999 0.730317 0.4236 0.899554 0.4618 0.992052 2.3162 0.614197 0.6534 0.932762 2.6758 0.546026 0.7007 0.922216 0.4579 0.917084 1.522 0.555799 0.4088 0.868125 0.4587 0.985392 1.7686 0.468988 0.6356 0.907352 2.0918 0.426854 0.6789 0.893525 0.4669 0.935109 2.484 0.907099 0.4709 1 0.4655 1 2.9144 0.772825 0.7005 1 3.4222 0.698337 0.7598 1 0.4272 0.855598

1.6512 0.60298 0.3896 0.827352 0.4533 0.973792 1.9565 0.518814 0.6264 0.894218 2.3011 0.469564 0.671 0.883127 0.4698 0.940917 1.8315 0.668821 0.3886 0.825228 0.4515 0.969925 2.2034 0.584286 0.6297 0.898929 2.6019 0.530946 0.6776 0.891814 0.4674 0.936111 1.7916 0.654251 0.4325 0.918454 0.4604 0.989044 2.0219 0.536157 0.6564 0.937045 2.2922 0.467748 0.7059 0.92906 0.4556 0.912477 2.0328 0.742331 0.4314 0.916118 0.4577 0.983244 2.3416 0.620933 0.6592 0.941042 2.6912 0.549168 0.7037 0.926165 0.4566 0.91448 2.2149 0.80883 0.3852 0.818008 0.4468 0.959828 2.7345 0.72512 0.6032 0.861099 3.3918 0.692133 0.6393 0.841406 0.4802 0.961746 2.137 0.780383 0.4034 0.856657 0.4514 0.96971 2.5681 0.680995 0.6291 0.898073 3.0858 0.629691 0.6721 0.884575 0.4695 0.940316

2.4073 0.87909 0.4214 0.894882 0.4569 0.981525 2.9412 0.779932 0.6488 0.926196 3.6209 0.738884 0.6994 0.920505 0.4585 0.918286 2.484 0.907099 0.4709 1 0.4655 1 2.9144 0.772825 0.7005 1 3.4222 0.698337 0.7598 1 0.4272 0.855598

2.5867 0.944603 0.3937 0.836059 0.4455 0.957035 3.1622 0.838535 0.644 0.919343 3.8522 0.786083 0.7069 0.930376 0.4552 0.911676 2.6253 0.958699 0.1424 0.3024 0.2953 0.634372 3.5487 0.941025 0.3441 0.491221 4.5385 0.92613 0.4534 0.596736 0.4978 0.996996

2.65 0.967718 0.1437 0.30516 0.2945 0.632653 3.6797 0.975763 0.3372 0.48137 4.7827 0.975962 0.4335 0.570545 0.4955 0.992389 2.6281 0.959721 0.1411 0.299639 0.2918 0.626853 3.6603 0.970619 0.3311 0.472662 4.7668 0.972717 0.4279 0.563175 0.4948 0.990987 2.507 0.915498 0.1428 0.303249 0.2956 0.635016 3.4575 0.916841 0.3409 0.486652 4.4604 0.910193 0.4507 0.593182 0.4975 0.996395

2.3718 0.866126 0.1286 0.273094 0.2811 0.603867 3.3233 0.881255 0.314 0.448251 4.3409 0.885808 0.4113 0.541327 0.492 0.98538 2.343 0.855609 0.1728 0.366957 0.3249 0.697959 3.1769 0.842433 0.4075 0.581727 4.0361 0.82361 0.5297 0.697157 0.4991 0.999599

2.2532 0.822816 0.1148 0.243788 0.2694 0.578733 3.1676 0.839967 0.2628 0.375161 4.2756 0.872482 0.3446 0.45354 0.4752 0.951732 2.7112 0.990067 0.3551 0.754088 0.4336 0.931472 3.4546 0.916072 0.611 0.872234 4.3057 0.878625 0.6763 0.890103 0.4679 0.937112 2.7384 1 0.1554 0.330006 0.307 0.659506 3.7545 0.995598 0.3644 0.5202 4.8162 0.982798 0.4739 0.623717 0.4993 1 1.751 0.639424 0.1331 0.28265 0.2861 0.614608 2.4051 0.637771 0.3258 0.465096 3.0776 0.628018 0.4251 0.559489 0.4943 0.989986

2.7192 0.992989 0.1462 0.310469 0.2966 0.637164 3.7711 1 0.3438 0.490792 4.9005 1 0.4446 0.585154 0.4969 0.995193

LBP1 LBP2 LBP3 LBP4 LBP5 LBP7 LBP8 LBP9 2.204 0.874222 0.3877 0.963469 0.4477 0.990268 2.7135 0.830197 0.6052 0.966619 3.3474 0.827193 0.6439 0.965802 0.4788 0.958175 2.159 0.856372 0.4024 1 0.4513 0.99823 2.6265 0.80358 0.6234 0.995688 3.2085 0.792868 0.6619 0.9928 0.4731 0.946768

2.0259 0.803578 0.3907 0.970924 0.449 0.993143 2.4857 0.760502 0.6111 0.976042 3.0918 0.76403 0.6399 0.959802 0.48 0.960576 1.8443 0.731546 0.395 0.98161 0.4521 1 2.2121 0.676794 0.6261 1 2.6407 0.652556 0.6667 1 0.4714 0.943366 1.8471 0.732656 0.1624 0.403579 0.3184 0.704269 2.3265 0.711794 0.3826 0.611084 2.8131 0.695159 0.4801 0.720114 0.4996 0.9998 1.7783 0.705367 0.1495 0.371521 0.3086 0.682592 2.2404 0.685452 0.3655 0.583773 2.7208 0.67235 0.4665 0.699715 0.4989 0.998399 1.9184 0.760938 0.15 0.372763 0.3079 0.681044 2.4411 0.746856 0.3553 0.567481 2.958 0.730966 0.4524 0.678566 0.4977 0.995998 1.9977 0.792392 0.1557 0.386928 0.3095 0.684583 2.5815 0.789812 0.3609 0.576425 3.2164 0.79482 0.4538 0.680666 0.4979 0.996398 2.3203 0.920352 0.1644 0.408549 0.3168 0.70073 2.9938 0.915955 0.3724 0.594793 3.7065 0.915931 0.4633 0.694915 0.4986 0.997799 1.9691 0.781048 0.1661 0.412773 0.3205 0.708914 2.4936 0.762919 0.3846 0.614279 3.0223 0.746855 0.4825 0.723714 0.4997 1 1.635 0.648526 0.1379 0.342694 0.2992 0.6618 2.0516 0.627689 0.3464 0.553266 2.4824 0.613438 0.449 0.673466 0.4974 0.995397

1.8384 0.729206 0.1441 0.358101 0.3031 0.670427 2.3421 0.716567 0.3428 0.547516 2.8381 0.701337 0.4392 0.658767 0.4963 0.993196 2.0491 0.81278 0.1571 0.390408 0.3105 0.686795 2.6446 0.809117 0.3646 0.582335 3.2747 0.809227 0.4569 0.685316 0.4981 0.996798 1.4928 0.592122 0.1184 0.294235 0.2806 0.620659 1.9078 0.583693 0.3087 0.493052 2.3265 0.574913 0.4127 0.619019 0.4923 0.985191 2.2869 0.907104 0.1643 0.4083 0.3167 0.700509 2.9469 0.901606 0.376 0.600543 3.6474 0.901327 0.4692 0.703765 0.499 0.998599 1.6408 0.650827 0.1439 0.357604 0.3031 0.670427 2.078 0.635766 0.3538 0.565085 2.5387 0.627351 0.4538 0.680666 0.4979 0.996398 1.6845 0.668161 0.1387 0.344682 0.2987 0.660695 2.1289 0.651339 0.3451 0.55119 2.6125 0.645588 0.4443 0.666417 0.4969 0.994397 1.8433 0.731149 0.1385 0.344185 0.295 0.652511 2.3548 0.720453 0.3333 0.532343 2.9035 0.717498 0.4205 0.630718 0.4936 0.987793 2.2046 0.87446 0.154 0.382704 0.3061 0.677063 2.8999 0.887227 0.3528 0.563488 3.6626 0.905083 0.4401 0.660117 0.4964 0.993396 1.6669 0.66118 0.152 0.377734 0.3115 0.689007 2.1088 0.645189 0.3728 0.595432 2.5717 0.635505 0.4708 0.706165 0.4991 0.998799 1.6718 0.663123 0.1446 0.359344 0.3043 0.673081 2.1096 0.645434 0.3559 0.56844 2.587 0.639286 0.4517 0.677516 0.4977 0.995998 2.5211 1 0.1886 0.468688 0.336 0.743198 3.2685 1 0.424 0.677208 4.0467 1 0.5243 0.786411 0.4994 0.9994 1.8609 0.73813 0.1565 0.388917 0.3145 0.695643 2.3471 0.718097 0.3734 0.59639 2.8511 0.704549 0.476 0.713964 0.4994 0.9994 1.9153 0.759708 0.143 0.355368 0.2986 0.660473 2.4501 0.74961 0.3395 0.542246 3.0324 0.749351 0.4241 0.636118 0.4942 0.988993

1.5 0.594978 0.1085 0.269632 0.2633 0.582393 1.9417 0.594065 0.2798 0.446893 2.468 0.60988 0.3422 0.513274 0.4744 0.94937 2.4132 0.957201 0.1611 0.400348 0.3116 0.689228 3.1873 0.975157 0.3589 0.573231 4.0178 0.992858 0.446 0.668967 0.4971 0.994797 1.6805 0.666574 0.1544 0.383698 0.3138 0.694094 2.1213 0.649013 0.3748 0.598626 2.5832 0.638347 0.4767 0.715014 0.4994 0.9994 1.6173 0.641506 0.1392 0.345924 0.2993 0.662022 2.0402 0.624201 0.3465 0.553426 2.4963 0.616873 0.4427 0.664017 0.4967 0.993996 1.7949 0.711951 0.1442 0.35835 0.3034 0.67109 2.2652 0.69304 0.3532 0.564127 2.7408 0.677293 0.4529 0.679316 0.4978 0.996198 2.5077 0.994685 0.1869 0.464463 0.335 0.740987 3.2526 0.995135 0.4232 0.67593 4.026 0.994885 0.5229 0.784311 0.4995 0.9996

45 | P a g e


(B0704498)

Appendix D: LTE features with their normalized value for normal and glaucoma images

LTE1 LTE2 LTE3 LTE4 LTE5 LTE6 LTE7 LTE8 4.61E+08 0.392237 1.95E+09 0.506664 6.13E+08 0.376921 6511004 0.150329 9951360 0.096084 2.68E+09 0.395735 16939356 0.229099 28521480 0.146268

4.23E+08 0.35932 1.93E+09 0.502073 1.31E+08 0.08052 5013039 0.115743 8590355 0.082943 1.5E+08 0.022114 14519587 0.196373 25380591 0.130161 4.2E+08 0.356713 1.92E+09 0.500664 82057878 0.05042 4857706 0.112157 8292630 0.080068 1.01E+08 0.014895 13902738 0.18803 24538398 0.125842

4.16E+08 0.35392 1.94E+09 0.504445 5.59E+08 0.343648 5019410 0.11589 9019250 0.087084 2.64E+09 0.389085 14550254 0.196788 26056078 0.133625 4.25E+08 0.361063 1.94E+09 0.50463 1.63E+09 1 5809303 0.134127 9655191 0.093224 1.68E+09 0.248243 16302239 0.220483 28299671 0.145131 7.89E+08 0.670846 3.84E+09 1 50468078 0.03101 3810226 0.087972 7374286 0.071201 60941714 0.008996 10570126 0.142958 20630450 0.1058 1.18E+09 1 1.17E+09 0.303857 60511563 0.037181 4338491 0.100169 7440571 0.071841 73644123 0.010871 12004995 0.162364 22040171 0.11303 4.04E+08 0.343574 1.93E+09 0.502401 5.46E+08 0.335461 4032222 0.093098 7407402 0.071521 2.62E+09 0.387301 11821658 0.159884 21403726 0.109766 29209872 0.024837 17712408 0.004612 38977712 0.02395 3394168 0.078366 5869488 0.056672 45597416 0.006731 9748896 0.131851 17596456 0.090241 4.04E+08 0.343094 6.28E+08 0.163502 4.45E+08 0.273563 43311800 1 1.04E+08 1 5.1E+08 0.075343 73938912 1 1.95E+08 1 39839660 0.033875 20227780 0.005267 48533624 0.029821 4281752 0.098859 6126632 0.059155 48388868 0.007143 9865436 0.133427 17808308 0.091327 67220240 0.057157 25213632 0.006565 79667872 0.048951 5783888 0.133541 6760544 0.065275 57323280 0.008462 10705376 0.144787 18722320 0.096015 31948290 0.027165 24088894 0.006272 1.06E+08 0.064994 4952354 0.114342 8184878 0.079028 1.46E+08 0.021506 14966562 0.202418 24998846 0.128203 36630140 0.031146 30777372 0.008013 66311388 0.040745 5967884 0.137789 10053500 0.09707 88367468 0.013045 16568172 0.224079 29880812 0.15324 81539704 0.069333 27416232 0.007138 1.65E+08 0.101311 5943052 0.137216 8505804 0.082126 1.96E+08 0.028915 15422616 0.208586 25702984 0.131814 64925379 0.055206 28699595 0.007472 1.19E+08 0.073341 6042711 0.139517 8881855 0.085757 1.51E+08 0.02231 15663683 0.211846 26592747 0.136377 2.44E+08 0.207835 4.62E+08 0.120397 2.15E+08 0.131884 24682672 0.569883 56811744 0.548536 2.62E+08 0.038617 50226000 0.67929 1.19E+08 0.609654 4.04E+08 0.343094 6.28E+08 0.163502 4.45E+08 0.273563 43311800 1 1.04E+08 1 5.1E+08 0.075343 73938912 1 1.95E+08 1 2.68E+08 0.227687 1.59E+08 0.041446 1.76E+08 0.108205 19474512 0.449635 27183820 0.262468 1.41E+08 0.020882 20818220 0.28156 41374008 0.212181 5.8E+08 0.493056 2.65E+09 0.688856 1.08E+09 0.660655 4396282 0.101503 3063650 0.029581 6.71E+09 0.990031 3895762 0.052689 3485330 0.017874

5.75E+08 0.489287 2.57E+09 0.670189 1.1E+09 0.675593 4781149 0.110389 3062525 0.02957 6.71E+09 0.990373 4184253 0.056591 3683253 0.018889 5.7E+08 0.485061 2.59E+09 0.675281 1.11E+09 0.684675 4526357 0.104506 2966501 0.028643 6.69E+09 0.986979 3976405 0.05378 3551469 0.018213

5.66E+08 0.481331 2.59E+09 0.674544 1.08E+09 0.664984 4264642 0.098464 2921778 0.028211 6.7E+09 0.989742 3822366 0.051696 3460078 0.017745 5.78E+08 0.491144 2.66E+09 0.692198 1.18E+09 0.725392 3662783 0.084568 2615459 0.025253 6.77E+09 1 3487215 0.047163 3227899 0.016554 4.01E+08 0.341364 1.74E+08 0.045427 3.95E+08 0.242616 25419944 0.586906 25759968 0.248721 2.71E+08 0.040014 14311004 0.193552 25165244 0.129056 3.2E+08 0.272496 67377092 0.017543 2.85E+08 0.175346 12125816 0.279966 5618664 0.05425 66744708 0.009853 5539492 0.07492 4485780 0.023005

6.31E+08 0.536293 3.45E+08 0.089936 3.82E+08 0.234964 33275398 0.768276 41254594 0.398326 2.7E+08 0.039917 22058302 0.298331 43120346 0.221137 5.47E+08 0.465287 2.49E+09 0.648047 1.13E+09 0.696753 5887438 0.135932 3640550 0.035151 6.69E+09 0.987195 4920358 0.066546 4114254 0.021099 5.4E+08 0.459146 2.61E+09 0.679663 1.16E+08 0.070971 2951065 0.068135 2423565 0.0234 62106041 0.009168 2222053 0.030053 2877945 0.014759 5.5E+08 0.468048 2.46E+09 0.641284 1.1E+09 0.672894 5180101 0.1196 3201485 0.030911 6.63E+09 0.978431 4345749 0.058775 3704613 0.018999

LTE1 LTE2 LTE3 LTE4 LTE5 LTE6 LTE7 LTE8 70679279 0.106833 27079987 0.015566 1.65E+08 0.332449 6038495 0.072322 8442115 0.037279 1.76E+08 0.174657 15495199 0.083336 25505635 0.050845

76865384 0.116183 27981260 0.016084 1.52E+08 0.306501 5817588 0.069677 8565344 0.037823 1.72E+08 0.170195 15473448 0.083219 25774156 0.051381 59321666 0.089666 23686046 0.013615 92934046 0.187654 5006242 0.059959 7435102 0.032832 87246638 0.086546 12972338 0.069767 22234702 0.044325 47834052 0.072302 23822440 0.013693 58666904 0.118461 4792100 0.057395 7134968 0.031507 58894888 0.058422 11513476 0.061921 20791976 0.041449 5.05E+08 0.762826 1.22E+09 0.703891 3.34E+08 0.673634 66121133 0.791926 1.64E+08 0.72373 6.52E+08 0.646914 1.41E+08 0.757669 3.82E+08 0.762055 5.72E+08 0.864553 1.44E+09 0.826875 4.35E+08 0.877817 75613171 0.905611 1.95E+08 0.860304 9.23E+08 0.916037 1.57E+08 0.846294 4.25E+08 0.847077 5.93E+08 0.895721 1.43E+09 0.823064 3.98E+08 0.803267 67838078 0.81249 1.46E+08 0.644637 8.32E+08 0.825767 1.46E+08 0.783434 3.5E+08 0.697492 5.63E+08 0.851638 1.38E+09 0.792401 3.71E+08 0.748267 65001727 0.778519 1.59E+08 0.70001 6.91E+08 0.685094 1.35E+08 0.727497 3.88E+08 0.77416 6.26E+08 0.946601 1.57E+09 0.901133 4.95E+08 1 77457635 0.927702 1.99E+08 0.880882 9.43E+08 0.935876 1.58E+08 0.851696 3.99E+08 0.795855 5.12E+08 0.774214 1.22E+09 0.704035 3.49E+08 0.705509 66361871 0.79481 1.63E+08 0.721687 6.61E+08 0.65547 1.41E+08 0.755954 3.84E+08 0.765221 5.69E+08 0.859628 1.45E+09 0.830928 4.25E+08 0.85773 75390162 0.902941 1.95E+08 0.862002 9.21E+08 0.913263 1.58E+08 0.84963 4.28E+08 0.853739 5.9E+08 0.891657 1.43E+09 0.824819 3.92E+08 0.792493 67891076 0.813125 1.47E+08 0.647288 8.3E+08 0.823559 1.47E+08 0.789387 3.54E+08 0.706298

5.75E+08 0.868989 1.39E+09 0.799465 3.72E+08 0.750474 66208687 0.792975 1.6E+08 0.707599 6.88E+08 0.682501 1.38E+08 0.739941 3.92E+08 0.781433 5.83E+08 0.88072 1.53E+09 0.877491 3.26E+08 0.657601 70622262 0.845836 1.86E+08 0.82023 6.13E+08 0.60821 1.44E+08 0.773086 4.5E+08 0.89684 6.18E+08 0.934115 1.55E+09 0.891402 4.87E+08 0.9828 76950701 0.921631 1.99E+08 0.87698 9.33E+08 0.925494 1.57E+08 0.845318 3.98E+08 0.792827 5.04E+08 0.762227 1.22E+09 0.698705 3.27E+08 0.659782 68796927 0.823974 1.73E+08 0.762888 6.65E+08 0.65999 1.48E+08 0.798304 3.94E+08 0.786411 5.64E+08 0.852459 1.43E+09 0.824823 4.43E+08 0.893749 78500375 0.940191 2.04E+08 0.90033 9.55E+08 0.947242 1.64E+08 0.884361 4.39E+08 0.874989 6.62E+08 1 1.74E+09 1 4.58E+08 0.925314 83154902 0.995938 2.26E+08 1 1.01E+09 1 1.75E+08 0.942192 4.47E+08 0.891672 5.7E+08 0.861459 1.3E+09 0.747338 4.09E+08 0.825387 65172883 0.780569 1.58E+08 0.697944 7.37E+08 0.731337 1.33E+08 0.715887 3.76E+08 0.74876

5.33E+08 0.806256 1.34E+09 0.771625 3.9E+08 0.788096 63962705 0.766075 1.67E+08 0.736957 8.21E+08 0.814455 1.33E+08 0.716251 3.5E+08 0.69784 5.58E+08 0.843968 1.51E+09 0.870719 4.01E+08 0.810261 74799997 0.895872 1.89E+08 0.836294 8.79E+08 0.872252 1.59E+08 0.857779 3.85E+08 0.768103 3.17E+08 0.479619 6.87E+08 0.394757 2.65E+08 0.535544 33912653 0.406168 96294021 0.425221 3.85E+08 0.381936 65188053 0.350591 1.97E+08 0.392851 5.82E+08 0.880454 1.47E+09 0.84326 4.57E+08 0.923375 80630454 0.965703 2.09E+08 0.922504 9.69E+08 0.960837 1.68E+08 0.903917 4.5E+08 0.897373 6.55E+08 0.989847 1.73E+09 0.991842 4.61E+08 0.929942 81757740 0.979204 2.24E+08 0.990688 1E+09 0.991965 1.72E+08 0.924154 4.39E+08 0.874542 5.53E+08 0.836537 1.4E+09 0.804283 4E+08 0.808081 83494051 1 2.17E+08 0.959832 8.52E+08 0.844663 1.86E+08 1 5.02E+08 1 5.88E+08 0.888505 1.3E+09 0.747227 4.5E+08 0.909018 65508543 0.784589 1.57E+08 0.692811 7.57E+08 0.750634 1.32E+08 0.709858 3.75E+08 0.747029 5.33E+08 0.805078 1.34E+09 0.768777 3.93E+08 0.792966 63846146 0.764679 1.67E+08 0.738565 8.22E+08 0.815127 1.32E+08 0.712546 3.5E+08 0.698695 5.55E+08 0.838761 1.52E+09 0.871096 3.96E+08 0.798907 74603134 0.893514 1.89E+08 0.832825 8.76E+08 0.868573 1.59E+08 0.855118 3.82E+08 0.761844 6.2E+08 0.936685 1.65E+09 0.950482 4.23E+08 0.854774 80061159 0.958885 2.09E+08 0.922911 9.08E+08 0.900757 1.67E+08 0.895854 4.19E+08 0.83501 3.4E+08 0.513859 7.48E+08 0.43013 2.66E+08 0.53797 36744914 0.44009 1.04E+08 0.460291 4.09E+08 0.405545 71886446 0.386616 2.15E+08 0.42801

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Appendix E: FGLCM features with their normalized value for normal and glaucoma images

Homogeneity Energy Entropy Contrast Symmetry Correlation 0.620989015 0.945079 11665520 0.901274 1.158518 0.945674 13.97965 0.766568 0.999489464 0.999489 0.008756294 0.921818 0.62392631 0.949549 11879820 0.917831 1.140618 0.931062 13.1026 0.718475 0.999846717 0.999847 0.008605746 0.905969 0.628815674 0.95699 12170163 0.940263 1.11826 0.912811 11.43646 0.627113 0.998871246 0.998871 0.008449537 0.889524 0.612657838 0.9324 11173373 0.863251 1.191646 0.972715 16.85927 0.92447 0.999409035 0.999409 0.009015927 0.949151 0.620813046 0.944811 11713911 0.905013 1.156598 0.944106 14.107 0.773551 0.998883048 0.998883 0.008732041 0.919265 0.614278558 0.934866 11245555 0.868828 1.186516 0.968528 16.26799 0.892048 0.999759133 0.999759 0.008979023 0.945266 0.613789847 0.934122 11225873 0.867307 1.187825 0.969596 16.45285 0.902185 0.999103116 0.999103 0.008987121 0.946118 0.616397442 0.938091 11459813 0.885382 1.171396 0.956185 15.71258 0.861592 0.99992399 0.999924 0.008832355 0.929825 0.609557964 0.927682 10763591 0.831592 1.216021 0.992612 17.57046 0.963469 0.999624157 0.999624 0.009283869 0.977359 0.62668512 0.953748 10186131 0.786977 1.200844 0.980223 10.04577 0.550856 0.999895889 0.999896 0.009487962 0.998844 0.607499342 0.924549 10622825 0.820716 1.225072 1 18.23667 1 0.999984842 0.999985 0.009372927 0.986734 0.608921288 0.926713 10641882 0.822188 1.223644 0.998834 17.63086 0.966781 0.999707103 0.999707 0.00938089 0.987572 0.616247472 0.937863 11367040 0.878214 1.17636 0.960237 15.6517 0.858254 0.99931897 0.999319 0.008887949 0.935678 0.61134247 0.930398 10669095 0.824291 1.221404 0.997005 16.5891 0.909656 0.999451216 0.999451 0.009399401 0.989521 0.614429658 0.935096 11217898 0.866691 1.186372 0.96841 16.2038 0.888528 0.999981265 0.999981 0.008979695 0.945337 0.612492462 0.932148 10908153 0.84276 1.206522 0.984858 16.51402 0.905539 0.999847141 0.999847 0.009208515 0.969426 0.626614531 0.95364 10173542 0.786005 1.201618 0.980855 10.05238 0.551218 0.999975259 0.999975 0.009498938 1 0.62668512 0.953748 10186131 0.786977 1.200844 0.980223 10.04577 0.550856 0.999895889 0.999896 0.009487962 0.998844 0.627177227 0.954496 11101463 0.857696 1.188495 0.970143 10.23541 0.561254 0.999157576 0.999158 0.009300323 0.979091 0.657074964 0.999998 12943297 0.999995 0.977717 0.79809 1.373723 0.075328 0.999998714 0.999999 0.007772395 0.818238 0.657073734 0.999996 12943249 0.999991 0.977729 0.798099 1.374206 0.075354 0.999997352 0.999997 0.007772409 0.81824 0.657073734 0.999996 12943249 0.999991 0.977729 0.798099 1.374206 0.075354 0.999997352 0.999997 0.007772409 0.81824 0.657072813 0.999994 12943214 0.999989 0.977737 0.798105 1.374552 0.075373 0.999995952 0.999996 0.007772422 0.818241 0.657073734 0.999996 12943249 0.999991 0.977729 0.798099 1.374206 0.075354 0.999997352 0.999997 0.007772409 0.81824 0.630964813 0.960261 11388174 0.879847 1.169571 0.954696 9.046504 0.496061 0.999497967 0.999498 0.009121223 0.960236 0.651762274 0.991912 9982462 0.771242 1.015497 0.828928 3.217399 0.176425 0.999999091 0.999999 0.007908786 0.832597 0.627772062 0.955402 11157411 0.862018 1.184945 0.967245 10.06945 0.552154 0.999273105 0.999273 0.00926214 0.975071 0.657071671 0.999993 12943167 0.999985 0.977747 0.798114 1.375007 0.075398 0.999996255 0.999996 0.007772434 0.818242 0.657076509 1 12943361 1 0.977703 0.798078 1.373134 0.075295 1 1 0.007772377 0.818236 0.657072813 0.999994 12943214 0.999989 0.977737 0.798105 1.374552 0.075373 0.999995952 0.999996 0.007772422 0.818241

Homogeneity Energy Entropy Contrast Symmetry Correlation 0.613575723 0.934042 11239406 0.824217 1.184908 0.967566 16.62697 0.93421 0.999537315 0.999543 0.008947952 0.953282 0.61307054 0.933273 10969916 0.804454 1.202548 0.981971 16.36664 0.919583 0.999683126 0.999689 0.009165248 0.976432 0.612622057 0.93259 10720017 0.786128 1.21798 0.994572 16.10674 0.90498 0.998907285 0.998913 0.009376473 0.998935 0.608490117 0.9263 10627192 0.779321 1.224627 1 17.79789 1 0.999915637 0.999921 0.009386466 1 0.646822456 0.984653 13340744 0.978313 1.026864 0.838511 5.218615 0.293215 0.999519358 0.999525 0.007933357 0.845191 0.650844517 0.990776 13530892 0.992258 1.009776 0.824558 3.712319 0.208582 0.999965292 0.999971 0.007869433 0.838381 0.647469168 0.985638 13363642 0.979993 1.025044 0.837025 4.964296 0.278926 0.998823666 0.998829 0.007927894 0.844609 0.649643398 0.988948 13470837 0.987854 1.015399 0.829149 4.157218 0.233579 0.999422493 0.999428 0.007890426 0.840617 0.653093934 0.9942 13636471 1 0.999577 0.81623 2.867489 0.161114 0.999462585 0.999468 0.007834894 0.834701 0.646344861 0.983926 13322716 0.976991 1.028309 0.839691 5.4046 0.303665 0.999538767 0.999545 0.007938126 0.845699 0.64939861 0.988575 13471323 0.987889 1.015045 0.82886 4.268175 0.239814 0.999922827 0.999929 0.007886627 0.840213 0.647815057 0.986164 13373327 0.980703 1.024335 0.836446 4.824124 0.27105 0.998646299 0.998652 0.007926629 0.844474 0.650165077 0.989742 13498311 0.989868 1.012815 0.827039 3.966246 0.222849 0.99949786 0.999504 0.007880405 0.83955 0.647733639 0.98604 13394495 0.982255 1.021884 0.834445 4.894764 0.275019 0.999824262 0.99983 0.007911883 0.842903 0.652813275 0.993773 13620194 0.998806 1.001271 0.817613 2.967988 0.166761 0.999470377 0.999476 0.007841127 0.835365 0.645101163 0.982033 13262199 0.972554 1.033516 0.843943 5.866981 0.329645 0.999670978 0.999677 0.007959399 0.847965 0.650934449 0.990913 13538619 0.992824 1.008958 0.82389 3.684137 0.206998 0.999400464 0.999406 0.007865826 0.837997 0.649289259 0.988409 13464868 0.987416 1.015668 0.829369 4.307131 0.242002 0.999683763 0.99969 0.007889172 0.840484 0.652202901 0.992844 13594600 0.996929 1.003704 0.8196 3.202167 0.179918 0.99934061 0.999346 0.007848545 0.836155 0.65278109 0.993724 13623244 0.99903 1.000859 0.817276 2.987358 0.167849 0.999640873 0.999647 0.007838751 0.835112 0.651945226 0.992452 13584135 0.996162 1.004679 0.820395 3.301528 0.185501 0.999635809 0.999642 0.007851475 0.836468 0.656891526 0.999981 8090442 0.593294 0.979033 0.799454 1.443503 0.081105 0.999994193 1 0.007763359 0.82708 0.651241375 0.99138 13554984 0.994024 1.007361 0.822586 3.572023 0.200699 0.99970944 0.999715 0.007859851 0.83736 0.649340338 0.988486 13458639 0.986959 1.016455 0.830012 4.274166 0.24015 0.999395293 0.999401 0.007893864 0.840984 0.650378066 0.990066 13510516 0.990763 1.011633 0.826074 3.889832 0.218556 0.999770287 0.999776 0.007875697 0.839048 0.652734325 0.993653 13613885 0.998344 1.001948 0.818166 2.993547 0.168197 0.999332853 0.999339 0.007843964 0.835667 0.652628018 0.993491 13616212 0.998514 1.001549 0.81784 3.045109 0.171094 0.999613567 0.999619 0.007840992 0.835351 0.651823665 0.992267 13577517 0.995677 1.005337 0.820933 3.345761 0.187986 0.999545496 0.999551 0.00785391 0.836727 0.650613051 0.990424 13522418 0.991636 1.010503 0.825152 3.803033 0.213679 0.999367456 0.999373 0.007871501 0.838601 0.656903758 1 8061464 0.591169 0.978979 0.79941 1.438666 0.080833 0.999915861 0.999922 0.007763175 0.827061

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Appendix F: FGLCM features with their normalized value for normal and glaucoma images

ShortRunEmphasis LongRunEmphasis RunPer GrayLvlNonUni RunLengthNonUni

0.880303221 0.982662 2.294175382 0.10737 2.421063 1 262507.6632 0.999209 4114.877255 0.480409 0.867652388 0.96854 2.296775056 0.107492 2.387554 0.98616 249553.7579 0.949902 3924.92705 0.458232 0.862213286 0.962469 2.472901944 0.115735 2.360307 0.974906 242810.6163 0.924234 4795.819818 0.559908 0.86084671 0.960943 2.227773815 0.104263 2.372928 0.980119 244499.5804 0.930663 7162.997215 0.836274 0.871087568 0.972375 2.273282611 0.106393 2.397403 0.990228 254161.3723 0.96744 4882.317752 0.570007 0.837732133 0.935141 2.498404775 0.116929 2.285041 0.943817 221308.037 0.842387 7022.196642 0.819836 0.843237916 0.941287 2.424958179 0.113491 2.306702 0.952764 226634.0554 0.86266 5383.723326 0.628545 0.841185873 0.938996 2.393571881 0.112022 2.29592 0.948311 224768.8751 0.85556 5749.648762 0.671267 0.829335131 0.925768 2.902802069 0.135855 2.217909 0.916089 210219.9492 0.800181 7790.997676 0.909593 0.873493005 0.97506 14.48843966 0.678078 2.086656 0.861876 221545.7677 0.843292 5342.031779 0.623678 0.829779772 0.926264 3.046216263 0.142567 2.203125 0.909983 209107.9471 0.795949 7821.869361 0.913197 0.842449204 0.940407 2.919835947 0.136652 2.253764 0.930899 221084.5757 0.841536 8565.370113 1 0.861814065 0.962023 2.431322504 0.113789 2.354173 0.972372 242803.593 0.924208 5917.695161 0.690886 0.872621814 0.974088 2.870545457 0.134345 2.346239 0.969095 249002.0401 0.947801 6644.159567 0.7757 0.873246288 0.974785 2.305837581 0.107916 2.398621 0.990731 254863.3345 0.970112 4562.803501 0.532704 0.873953471 0.975574 2.506766742 0.11732 2.381748 0.983761 253585.2485 0.965247 4848.354778 0.566041 0.870445428 0.971658 14.95102881 0.699728 2.06783 0.8541 217824.4464 0.829127 5041.151767 0.58855 0.873493005 0.97506 14.48843966 0.678078 2.086656 0.861876 221545.7677 0.843292 5342.031779 0.623678 0.89583511 1 12.30988426 0.576119 2.335653 0.964722 262715.3895 1 5012.668118 0.585225 0.836825594 0.934129 5.933628127 0.277702 2.041294 0.84314 197249.008 0.750809 3367.513733 0.393154 0.850604077 0.94951 5.265071665 0.246412 2.088501 0.862638 209209.2038 0.796334 2995.078393 0.349673 0.84887593 0.947581 5.387711216 0.252152 2.081394 0.859703 207545.0744 0.79 2988.654476 0.348923 0.842003624 0.939909 6.23001867 0.291573 2.055922 0.849182 201330.1396 0.766343 4414.004024 0.515331 0.82104235 0.916511 6.420987829 0.300511 1.960849 0.809913 181909.5513 0.692421 3147.747426 0.367497 0.841224637 0.93904 13.19975214 0.617766 2.134196 0.881512 208533.8146 0.793763 5654.354331 0.660141 0.840975241 0.938761 21.36691357 1 1.37491 0.567895 132247.2735 0.503386 2270.278744 0.265053 0.886876332 0.99 12.22673228 0.572227 2.312324 0.955086 254090.3253 0.96717 6229.239848 0.727259 0.854011827 0.953314 5.137331543 0.240434 2.095913 0.8657 211870.4225 0.806464 3628.324865 0.423604 0.825956209 0.921996 5.911899169 0.276685 1.989224 0.821633 186905.9812 0.711439 3287.006048 0.383755 0.853329153 0.952552 5.012206985 0.234578 2.098341 0.866703 211713.735 0.805867 2711.573654 0.316574

ShortRunEmphasis LongRunEmphasis RunPer GrayLvlNonUni RunLengthNonUni

0.873527874 1 2.279775668 0.142643 2.399102 1 255130.4442 1 4721.800256 0.688833 0.873468987 0.999933 2.415206074 0.151117 2.386556 0.99477 253770.849 0.994671 5541.731399 0.808447 0.866019814 0.991405 2.911854334 0.182192 2.323351 0.968425 242377.3305 0.950013 6798.549341 0.991796 0.851125774 0.974354 2.782755972 0.174114 2.286357 0.953005 229426.2186 0.899251 6854.783767 1 0.84172751 0.963595 11.37213982 0.711542 2.082981 0.868233 203895.3015 0.799181 3319.625021 0.484279 0.821648107 0.940609 13.22090314 0.827217 2.014214 0.83957 187100.792 0.733353 4107.629209 0.599235 0.833029566 0.953638 12.92425079 0.808656 2.002475 0.834677 191812.1594 0.75182 3804.034913 0.554946 0.852403332 0.975817 11.09559853 0.694239 2.136902 0.890709 214985.4209 0.842649 4166.996268 0.607896 0.85979327 0.984277 8.400551367 0.525613 2.209039 0.920777 226659.6894 0.888407 3282.11634 0.478807 0.852485754 0.975911 10.56346316 0.660944 2.132128 0.888719 214593.1875 0.841112 3186.569732 0.464868 0.796682103 0.912028 13.38007255 0.837176 1.914497 0.798005 166599.9166 0.652999 4090.586056 0.596749 0.825271163 0.944757 13.45696798 0.841988 1.961127 0.817442 184087.8843 0.721544 4004.896037 0.584248 0.854711594 0.978459 12.0843646 0.756105 2.13777 0.891071 216369.8238 0.848075 4188.171924 0.610985 0.759961731 0.869991 15.26967018 0.955406 1.732727 0.72224 136769.3192 0.536076 5085.228383 0.741851 0.858000392 0.982224 8.454761171 0.529005 2.200656 0.917283 224724.6258 0.880822 3858.320629 0.562865 0.809614035 0.926833 11.85063978 0.741481 1.959947 0.81695 176488.4568 0.691758 3147.008394 0.459097 0.808097044 0.925096 14.56916714 0.911577 1.957818 0.816063 175547.0513 0.688068 4082.947279 0.595635 0.827653008 0.947483 12.95489581 0.810574 2.022739 0.843123 190869.5652 0.748125 4940.645765 0.720759 0.857317648 0.981443 10.02249001 0.627096 2.172309 0.905468 221403.4982 0.867805 2749.975431 0.401176 0.816781325 0.935038 11.89201433 0.74407 2.032891 0.847355 186441.4599 0.730769 4099.465182 0.598044 0.812863586 0.930553 13.5888691 0.850241 1.995226 0.831655 181117.9426 0.709903 6253.495146 0.912282 0.858057489 0.98229 1.894929686 0.118564 1.799197 0.749946 146830.7365 0.575512 4317.997909 0.629925 0.83076323 0.951044 12.59405999 0.787996 2.056424 0.857164 195592.8618 0.766639 6031.651105 0.879919 0.83647071 0.957578 12.0392104 0.75328 2.062676 0.85977 199154.6448 0.780599 4752.400409 0.693297 0.773415692 0.885393 15.9823815 1 1.826823 0.761461 149447.899 0.585771 3963.589525 0.578222 0.868245136 0.993952 8.636533142 0.540378 2.225939 0.927822 233413.9386 0.914881 2751.12116 0.401343 0.818270667 0.936742 11.4544427 0.716692 2.03991 0.850281 187813.0163 0.736145 4005.065666 0.584273 0.805998368 0.922693 13.39957438 0.838397 1.963013 0.818228 175027.1383 0.68603 6050.070781 0.882606 0.818998419 0.937576 13.57414053 0.849319 1.991957 0.830293 183779.0886 0.720334 4844.101053 0.706675 0.854867362 0.978638 1.900971298 0.118942 1.784894 0.743984 144469.984 0.566259 5086.151401 0.741986

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Appendix G: PCA features for normal and glaucoma images used in PNN classification

PCA1 PCA2 PCA3 PCA4 PCA5 PCA6 PCA7 PCA8 PCA9 PCA10 PCA11 PCA12

0.315691 0.3149 0.303742 0.298353 0.289143 0.276636 0.273371 0.261364 0.26077 0.237509 0.216965 0.179418 0.387624 0.375074 0.372248 0.370004 0.351365 0.351013 0.350392 0.332526 0.319295 0.307433 0.291257 0.269719 0.352907 0.332928 0.327812 0.323149 0.315375 0.309897 0.29317 0.287615 0.277141 0.265718 0.260259 0.247221 0.283664 0.282687 0.281346 0.275283 0.263511 0.251719 0.238078 0.234968 0.233232 0.22992 0.227039 0.224179 0.267621 0.264991 0.257848 0.257179 0.25717 0.253872 0.242057 0.239995 0.235061 0.213745 0.212432 0.211726 0.346235 0.328405 0.314763 0.310064 0.291501 0.290052 0.287353 0.281376 0.281101 0.251588 0.240655 0.238283 0.311566 0.294595 0.281162 0.280655 0.26433 0.257685 0.252853 0.247276 0.245688 0.223629 0.21615 0.213751 0.348318 0.32171 0.312451 0.304577 0.295262 0.294357 0.289028 0.286091 0.278482 0.27335 0.25582 0.241648 0.380862 0.373642 0.365609 0.351719 0.351719 0.348821 0.348821 0.348774 0.342991 0.323359 0.315932 0.314018 0.275574 0.274419 0.256336 0.255798 0.250634 0.229322 0.182673 0.137451 0.131172 0.118866 0.062552 0.048399 0.399911 0.386645 0.373702 0.357235 0.340828 0.326175 0.324459 0.309893 0.294129 0.283038 0.227262 0.220627 0.375921 0.364659 0.347011 0.343868 0.343867 0.341099 0.341099 0.341053 0.338932 0.317493 0.313197 0.307985 0.429451 0.412779 0.403083 0.40243 0.400644 0.37827 0.377452 0.376085 0.369467 0.364615 0.358861 0.352884 0.350457 0.342973 0.336696 0.32754 0.322547 0.321064 0.301254 0.294494 0.28385 0.279617 0.26957 0.267932 0.37276 0.356814 0.356563 0.352142 0.350439 0.343776 0.341858 0.327366 0.317126 0.270558 0.270558 0.261909

0.374165 0.373068 0.364881 0.360657 0.359017 0.358733 0.354554 0.329624 0.321455 0.294846 0.294846 0.28738 0.345278 0.330778 0.326803 0.326133 0.326039 0.316936 0.298918 0.271473 0.265783 0.263563 0.24016 0.224368 0.275574 0.274419 0.256336 0.255798 0.250634 0.229322 0.182673 0.137451 0.131172 0.118866 0.062552 0.048399 0.423692 0.4039 0.402783 0.393835 0.388414 0.380727 0.378189 0.368295 0.354068 0.343035 0.335368 0.281388 0.481232 0.481229 0.47823 0.471265 0.45346 0.439933 0.422259 0.415363 0.407364 0.324374 0.299472 0.279324 0.477969 0.477964 0.470363 0.468346 0.454905 0.453935 0.453736 0.445692 0.427483 0.340611 0.296213 0.276072 0.47964 0.479635 0.470631 0.466623 0.455838 0.452361 0.450262 0.439365 0.427224 0.339346 0.297884 0.277743

0.483835 0.483829 0.480235 0.473582 0.456113 0.42375 0.400682 0.399339 0.394033 0.333022 0.302082 0.281946 0.493716 0.493712 0.493707 0.479095 0.461743 0.410226 0.379523 0.374971 0.359842 0.311956 0.303628 0.291815 0.45686 0.433178 0.417522 0.417497 0.411957 0.396301 0.338773 0.337108 0.31287 0.299694 0.280871 0.251024

0.550403 0.542315 0.502135 0.489164 0.422885 0.39037 0.383 0.379331 0.373219 0.321645 0.296438 0.129135 0.379446 0.379379 0.372309 0.36445 0.356625 0.356549 0.344781 0.344466 0.326491 0.320851 0.305452 0.279482 0.470822 0.470815 0.470807 0.46642 0.458017 0.45362 0.449046 0.424136 0.336522 0.289065 0.277286 0.268936 0.565881 0.555867 0.530725 0.487877 0.387514 0.384117 0.363959 0.27732 0.25845 0.245544 0.205305 0.203652 0.476084 0.476078 0.476072 0.471277 0.469072 0.454677 0.454515 0.428635 0.342786 0.294325 0.281951 0.274189

PCA1 PCA2 PCA3 PCA4 PCA5 PCA6 PCA7 PCA8 PCA9 PCA10 PCA11 PCA12

0.364783 0.356189 0.355051 0.33235 0.331402 0.330585 0.328253 0.322958 0.318066 0.298994 0.298993 0.298826 0.364783 0.356189 0.355051 0.33235 0.331402 0.330585 0.328253 0.322958 0.318066 0.298994 0.298993 0.298826 0.312774 0.31171 0.307347 0.305918 0.304179 0.299061 0.288817 0.283699 0.2812 0.273351 0.272576 0.262788 0.356876 0.338486 0.337292 0.33123 0.327561 0.309881 0.300242 0.283176 0.256126 0.136197 0.088421 0.033669 0.327228 0.313151 0.312082 0.305742 0.291024 0.195662 0.172619 0.16594 0.144415 0.126609 0.119355 0.090254 0.333888 0.327746 0.326265 0.318582 0.276098 0.251526 0.2411 0.213306 0.200041 0.195793 0.182566 0.181782 0.281673 0.272995 0.271313 0.265668 0.239314 0.181397 0.154653 0.154652 0.130285 0.122701 0.120353 0.111443 0.316231 0.310065 0.308781 0.307687 0.29565 0.210542 0.171471 0.162482 0.160451 0.148983 0.133806 0.114654 0.286286 0.284085 0.283093 0.280487 0.270563 0.232888 0.222162 0.213989 0.207064 0.206639 0.202242 0.202218 0.318963 0.30802 0.302889 0.295954 0.294874 0.207682 0.164662 0.160074 0.158654 0.135675 0.113772 0.100011 0.341289 0.334467 0.333781 0.324805 0.259155 0.25792 0.248832 0.207894 0.20552 0.203622 0.199631 0.195522 0.284526 0.277722 0.27069 0.26978 0.265229 0.229283 0.176183 0.129 0.126514 0.120972 0.109345 0.108085 0.315415 0.309976 0.308486 0.308359 0.297077 0.209688 0.187607 0.166692 0.158167 0.145656 0.132864 0.131397 0.346801 0.345952 0.343016 0.335259 0.316167 0.2576 0.24148 0.238251 0.230752 0.206596 0.203664 0.195205 0.287686 0.287479 0.2847 0.282653 0.27168 0.271104 0.222995 0.214374 0.210511 0.206163 0.195984 0.190486 0.367442 0.353077 0.351601 0.343598 0.297877 0.21901 0.214988 0.195018 0.189127 0.187995 0.169348 0.157455 0.303265 0.301692 0.299781 0.289472 0.25611 0.249059 0.233964 0.220445 0.209198 0.202617 0.193229 0.183857 0.292233 0.288171 0.280642 0.280026 0.279649 0.27758 0.239716 0.234426 0.217547 0.183905 0.135356 0.132717 0.313364 0.311409 0.30983 0.309278 0.297877 0.221902 0.221517 0.203661 0.190894 0.184239 0.177894 0.15259 0.354575 0.354345 0.349269 0.335586 0.290583 0.2029 0.190657 0.172821 0.17 0.169792 0.161802 0.143641 0.329387 0.329222 0.325676 0.313547 0.263777 0.229097 0.205477 0.203943 0.191004 0.183465 0.177192 0.169692 0.463417 0.463398 0.462816 0.462243 0.445706 0.351088 0.290497 0.262871 0.249827 0.213362 0.206615 0.140625 0.305624 0.300249 0.297605 0.291619 0.271927 0.267062 0.257268 0.229599 0.228729 0.210142 0.203597 0.186679 0.270121 0.269998 0.268845 0.268003 0.26715 0.266642 0.265523 0.265115 0.257361 0.235734 0.208098 0.20231 0.341841 0.332605 0.331907 0.314884 0.301673 0.291211 0.227235 0.186504 0.18089 0.178378 0.167916 0.156636 0.304888 0.303232 0.299685 0.298841 0.298541 0.297747 0.280045 0.262089 0.23271 0.219769 0.213906 0.193393 0.352756 0.351871 0.346848 0.333632 0.290099 0.203637 0.188708 0.171197 0.168484 0.164864 0.158434 0.146323 0.330959 0.329279 0.327549 0.314055 0.257976 0.228797 0.206379 0.203855 0.1904 0.174043 0.173679 0.172009 0.293274 0.288713 0.2875 0.280217 0.255961 0.247559 0.234652 0.233762 0.219988 0.197833 0.192931 0.151851 0.453689 0.453289 0.450073 0.448825 0.448574 0.448374 0.432327 0.343004 0.28075 0.2531 0.238 0.197673

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Appendix H: Training1 data used in PNN classification

0.364783 0.356189 0.355051 0.33235 0.331402 0.330585 0.328253 0.322958 0.318066 0.298994 0.298993 0.298826 1 0.315691 0.3149 0.303742 0.298353 0.289143 0.276636 0.273371 0.261364 0.26077 0.237509 0.216965 0.179418 2 0.387624 0.375074 0.372248 0.370004 0.351365 0.351013 0.350392 0.332526 0.319295 0.307433 0.298993 0.298826 1 0.267621 0.264991 0.257848 0.257179 0.25717 0.195662 0.172619 0.16594 0.144415 0.126609 0.119355 0.090254 2 0.352907 0.332928 0.327812 0.323149 0.315375 0.309897 0.29317 0.287615 0.2812 0.273351 0.272576 0.262788 1 0.312774 0.31171 0.307347 0.305918 0.304179 0.299061 0.288817 0.283699 0.277141 0.265718 0.260259 0.247221 2 0.356876 0.338486 0.337292 0.33123 0.327561 0.309881 0.300242 0.283176 0.256126 0.22992 0.227039 0.224179 1 0.283664 0.282687 0.281346 0.275283 0.263511 0.251719 0.238078 0.234968 0.233232 0.136197 0.088421 0.033669 2 0.327228 0.313151 0.312082 0.305742 0.291024 0.253872 0.242057 0.239995 0.235061 0.213745 0.212432 0.211726 1 0.267621 0.264991 0.257848 0.257179 0.25717 0.195662 0.172619 0.16594 0.144415 0.126609 0.119355 0.090254 2 0.346235 0.328405 0.326265 0.318582 0.291501 0.290052 0.287353 0.281376 0.281101 0.251588 0.240655 0.238283 1 0.286286 0.284085 0.283093 0.280487 0.270563 0.232888 0.2411 0.213306 0.200041 0.195793 0.182566 0.181782 2 0.311566 0.294595 0.281162 0.280655 0.26433 0.257685 0.252853 0.247276 0.245688 0.223629 0.21615 0.213751 1 0.281673 0.272995 0.271313 0.265668 0.239314 0.181397 0.154653 0.154652 0.130285 0.122701 0.120353 0.111443 2 0.348318 0.32171 0.312451 0.307687 0.29565 0.294357 0.289028 0.286091 0.278482 0.27335 0.25582 0.241648 1 0.316231 0.310065 0.308781 0.304577 0.295262 0.210542 0.171471 0.162482 0.160451 0.148983 0.133806 0.114654 2 0.380862 0.373642 0.365609 0.351719 0.351719 0.348821 0.348821 0.348774 0.342991 0.323359 0.315932 0.314018 1 0.286286 0.284085 0.283093 0.280487 0.270563 0.232888 0.222162 0.213989 0.207064 0.206639 0.202242 0.202218 2 0.364783 0.356189 0.355051 0.33235 0.331402 0.330585 0.328253 0.322958 0.318066 0.298994 0.298993 0.298826 1 0.315691 0.3149 0.303742 0.298353 0.289143 0.276636 0.273371 0.261364 0.26077 0.237509 0.216965 0.179418 2 0.387624 0.375074 0.372248 0.370004 0.351365 0.351013 0.350392 0.332526 0.319295 0.307433 0.298993 0.298826 1 0.364783 0.356189 0.355051 0.33235 0.331402 0.330585 0.328253 0.322958 0.318066 0.298994 0.291257 0.269719 2 0.352907 0.332928 0.327812 0.323149 0.315375 0.309897 0.29317 0.287615 0.2812 0.273351 0.272576 0.262788 1 0.312774 0.31171 0.307347 0.305918 0.304179 0.299061 0.288817 0.283699 0.277141 0.265718 0.260259 0.247221 2 0.356876 0.338486 0.337292 0.33123 0.327561 0.309881 0.300242 0.283176 0.256126 0.22992 0.227039 0.224179 1 0.283664 0.282687 0.281346 0.275283 0.263511 0.251719 0.238078 0.234968 0.233232 0.136197 0.088421 0.033669 2 0.327228 0.313151 0.312082 0.305742 0.291024 0.253872 0.242057 0.239995 0.235061 0.213745 0.212432 0.211726 1 0.267621 0.264991 0.257848 0.257179 0.25717 0.195662 0.172619 0.16594 0.144415 0.126609 0.119355 0.090254 2 0.346235 0.328405 0.326265 0.318582 0.291501 0.290052 0.287353 0.281376 0.281101 0.251588 0.240655 0.238283 1 0.281673 0.272995 0.271313 0.265668 0.239314 0.181397 0.2411 0.213306 0.200041 0.195793 0.182566 0.181782 2 0.311566 0.294595 0.281162 0.280655 0.26433 0.257685 0.252853 0.247276 0.245688 0.223629 0.21615 0.213751 1 0.281673 0.272995 0.271313 0.265668 0.239314 0.181397 0.154653 0.154652 0.130285 0.122701 0.120353 0.111443 2 0.348318 0.32171 0.312451 0.307687 0.29565 0.294357 0.289028 0.286091 0.278482 0.27335 0.25582 0.241648 1 0.316231 0.310065 0.308781 0.304577 0.295262 0.210542 0.171471 0.162482 0.160451 0.148983 0.133806 0.114654 2 0.380862 0.373642 0.365609 0.351719 0.351719 0.348821 0.348821 0.348774 0.342991 0.323359 0.315932 0.314018 1 0.286286 0.284085 0.283093 0.280487 0.270563 0.232888 0.222162 0.213989 0.207064 0.206639 0.202242 0.202218 2 0.318963 0.30802 0.302889 0.295954 0.294874 0.229322 0.182673 0.160074 0.158654 0.135675 0.113772 0.100011 1 0.275574 0.274419 0.256336 0.255798 0.250634 0.207682 0.164662 0.137451 0.131172 0.118866 0.062552 0.048399 2 0.481232 0.481229 0.47823 0.471265 0.45346 0.439933 0.422259 0.415363 0.407364 0.324374 0.299472 0.279324 1 0.354575 0.354345 0.349269 0.335586 0.290583 0.2029 0.190657 0.172821 0.17 0.169792 0.161802 0.143641 2 0.477969 0.477964 0.470363 0.468346 0.454905 0.453935 0.453736 0.445692 0.427483 0.340611 0.296213 0.276072 1 0.329387 0.329222 0.325676 0.313547 0.263777 0.229097 0.205477 0.203943 0.191004 0.183465 0.177192 0.169692 2 Legend: 1(Normal Subject)

2(Glaucoma Subject)

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Appendix I: Training2 data used in PNN classification

0.364783 0.356189 0.355051 0.33235 0.331402 0.330585 0.328253 0.322958 0.318066 0.298994 0.298993 0.298826 1 0.315691 0.3149 0.303742 0.298353 0.289143 0.276636 0.273371 0.261364 0.26077 0.237509 0.216965 0.179418 2 0.387624 0.375074 0.372248 0.370004 0.351365 0.351013 0.350392 0.332526 0.319295 0.307433 0.298993 0.298826 1 0.364783 0.356189 0.355051 0.33235 0.331402 0.330585 0.328253 0.322958 0.318066 0.298994 0.291257 0.269719 2 0.352907 0.332928 0.327812 0.323149 0.315375 0.309897 0.29317 0.287615 0.2812 0.273351 0.272576 0.262788 1 0.312774 0.31171 0.307347 0.305918 0.304179 0.299061 0.288817 0.283699 0.277141 0.265718 0.260259 0.247221 2 0.356876 0.338486 0.337292 0.33123 0.327561 0.309881 0.300242 0.283176 0.256126 0.22992 0.227039 0.224179 1 0.283664 0.282687 0.281346 0.275283 0.263511 0.251719 0.238078 0.234968 0.233232 0.136197 0.088421 0.033669 2 0.327228 0.313151 0.312082 0.305742 0.291024 0.253872 0.242057 0.239995 0.235061 0.213745 0.212432 0.211726 1 0.267621 0.264991 0.257848 0.257179 0.25717 0.195662 0.172619 0.16594 0.144415 0.126609 0.119355 0.090254 2 0.346235 0.328405 0.326265 0.318582 0.291501 0.290052 0.287353 0.281376 0.281101 0.251588 0.240655 0.238283 1 0.333888 0.327746 0.314763 0.310064 0.276098 0.251526 0.2411 0.213306 0.200041 0.195793 0.182566 0.181782 2 0.311566 0.294595 0.281162 0.280655 0.26433 0.257685 0.252853 0.247276 0.245688 0.223629 0.21615 0.213751 1 0.281673 0.272995 0.271313 0.265668 0.239314 0.181397 0.154653 0.154652 0.130285 0.122701 0.120353 0.111443 2 0.348318 0.32171 0.312451 0.307687 0.29565 0.294357 0.289028 0.286091 0.278482 0.27335 0.25582 0.241648 1 0.316231 0.310065 0.308781 0.304577 0.295262 0.210542 0.171471 0.162482 0.160451 0.148983 0.133806 0.114654 2 0.380862 0.373642 0.365609 0.351719 0.351719 0.348821 0.348821 0.348774 0.342991 0.323359 0.315932 0.314018 1 0.286286 0.284085 0.283093 0.280487 0.270563 0.232888 0.222162 0.213989 0.207064 0.206639 0.202242 0.202218 2 0.318963 0.30802 0.302889 0.295954 0.294874 0.229322 0.182673 0.160074 0.158654 0.135675 0.113772 0.100011 1 0.275574 0.274419 0.256336 0.255798 0.250634 0.207682 0.164662 0.137451 0.131172 0.118866 0.062552 0.048399 2 0.481232 0.481229 0.47823 0.471265 0.45346 0.439933 0.422259 0.415363 0.407364 0.324374 0.299472 0.279324 1 0.354575 0.354345 0.349269 0.335586 0.290583 0.2029 0.190657 0.172821 0.17 0.169792 0.161802 0.143641 2 0.477969 0.477964 0.470363 0.468346 0.454905 0.453935 0.453736 0.445692 0.427483 0.340611 0.296213 0.276072 1 0.329387 0.329222 0.325676 0.313547 0.263777 0.229097 0.205477 0.203943 0.191004 0.183465 0.177192 0.169692 2 0.47964 0.479635 0.470631 0.466623 0.455838 0.452361 0.450262 0.439365 0.427224 0.339346 0.297884 0.277743 1

0.463417 0.463398 0.462816 0.462243 0.445706 0.351088 0.290497 0.262871 0.249827 0.213362 0.206615 0.140625 2 0.483835 0.483829 0.480235 0.473582 0.456113 0.42375 0.400682 0.399339 0.394033 0.333022 0.302082 0.281946 1 0.305624 0.300249 0.297605 0.291619 0.271927 0.267062 0.257268 0.229599 0.228729 0.210142 0.203597 0.186679 2 0.493716 0.493712 0.493707 0.479095 0.461743 0.410226 0.379523 0.374971 0.359842 0.311956 0.303628 0.291815 1 0.270121 0.269998 0.268845 0.268003 0.26715 0.266642 0.265523 0.265115 0.257361 0.235734 0.208098 0.20231 2 0.45686 0.433178 0.417522 0.417497 0.411957 0.396301 0.338773 0.337108 0.31287 0.299694 0.280871 0.251024 1

0.341841 0.332605 0.331907 0.314884 0.301673 0.291211 0.227235 0.186504 0.18089 0.178378 0.167916 0.156636 2 0.550403 0.542315 0.502135 0.489164 0.422885 0.39037 0.383 0.379331 0.373219 0.321645 0.296438 0.193393 1 0.304888 0.303232 0.299685 0.298841 0.298541 0.297747 0.280045 0.262089 0.23271 0.219769 0.213906 0.129135 2 0.379446 0.379379 0.372309 0.36445 0.356625 0.356549 0.344781 0.344466 0.326491 0.320851 0.305452 0.279482 1 0.352756 0.351871 0.346848 0.333632 0.290099 0.203637 0.188708 0.171197 0.168484 0.164864 0.158434 0.146323 2 0.470822 0.470815 0.470807 0.46642 0.458017 0.45362 0.449046 0.424136 0.336522 0.289065 0.277286 0.268936 1 0.330959 0.329279 0.327549 0.314055 0.257976 0.228797 0.206379 0.203855 0.1904 0.174043 0.173679 0.172009 2 0.565881 0.555867 0.530725 0.487877 0.387514 0.384117 0.363959 0.27732 0.25845 0.245544 0.205305 0.203652 1 0.293274 0.288713 0.2875 0.280217 0.255961 0.247559 0.234652 0.233762 0.219988 0.197833 0.192931 0.151851 2 0.476084 0.476078 0.476072 0.471277 0.469072 0.454677 0.454515 0.428635 0.342786 0.294325 0.281951 0.274189 1 0.453689 0.453289 0.450073 0.448825 0.448574 0.448374 0.432327 0.343004 0.28075 0.2531 0.238 0.197673 2 Legend: 1(Normal Subject)

2(Glaucoma Subject)

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Appendix J: Training3 data used in PNN classification

0.364783 0.356189 0.355051 0.33235 0.331402 0.330585 0.328253 0.322958 0.318066 0.298994 0.298993 0.298826 1 0.315691 0.3149 0.303742 0.298353 0.289143 0.276636 0.273371 0.261364 0.26077 0.237509 0.216965 0.179418 2 0.387624 0.375074 0.372248 0.370004 0.351365 0.351013 0.350392 0.332526 0.319295 0.307433 0.298993 0.298826 1 0.364783 0.356189 0.355051 0.33235 0.331402 0.330585 0.328253 0.322958 0.318066 0.298994 0.291257 0.269719 2 0.352907 0.332928 0.327812 0.323149 0.315375 0.309897 0.29317 0.287615 0.2812 0.273351 0.272576 0.262788 1 0.312774 0.31171 0.307347 0.305918 0.304179 0.299061 0.288817 0.283699 0.277141 0.265718 0.260259 0.247221 2 0.356876 0.338486 0.337292 0.33123 0.327561 0.309881 0.300242 0.283176 0.256126 0.22992 0.227039 0.224179 1 0.283664 0.282687 0.281346 0.275283 0.263511 0.251719 0.238078 0.234968 0.233232 0.136197 0.088421 0.033669 2 0.327228 0.313151 0.312082 0.305742 0.291024 0.253872 0.242057 0.239995 0.235061 0.213745 0.212432 0.211726 1 0.267621 0.264991 0.257848 0.257179 0.25717 0.195662 0.172619 0.16594 0.144415 0.126609 0.119355 0.090254 2 0.346235 0.328405 0.326265 0.318582 0.291501 0.290052 0.287353 0.281376 0.281101 0.251588 0.240655 0.238283 1 0.333888 0.327746 0.314763 0.310064 0.276098 0.251526 0.2411 0.213306 0.200041 0.195793 0.182566 0.181782 2 0.311566 0.294595 0.281162 0.280655 0.26433 0.257685 0.252853 0.247276 0.245688 0.223629 0.21615 0.213751 1 0.281673 0.272995 0.271313 0.265668 0.239314 0.181397 0.154653 0.154652 0.130285 0.122701 0.120353 0.111443 2 0.348318 0.32171 0.312451 0.307687 0.29565 0.294357 0.289028 0.286091 0.278482 0.27335 0.25582 0.241648 1 0.316231 0.310065 0.308781 0.304577 0.295262 0.210542 0.171471 0.162482 0.160451 0.148983 0.133806 0.114654 2 0.380862 0.373642 0.365609 0.351719 0.351719 0.348821 0.348821 0.348774 0.342991 0.323359 0.315932 0.314018 1 0.286286 0.284085 0.283093 0.280487 0.270563 0.232888 0.222162 0.213989 0.207064 0.206639 0.202242 0.202218 2 0.364783 0.356189 0.355051 0.33235 0.331402 0.330585 0.328253 0.322958 0.318066 0.298994 0.298993 0.298826 1 0.315691 0.3149 0.303742 0.298353 0.289143 0.276636 0.273371 0.261364 0.26077 0.237509 0.216965 0.179418 2 0.481232 0.481229 0.47823 0.471265 0.45346 0.439933 0.422259 0.415363 0.407364 0.324374 0.299472 0.279324 1 0.354575 0.354345 0.349269 0.335586 0.290583 0.2029 0.190657 0.172821 0.17 0.169792 0.161802 0.143641 2 0.477969 0.477964 0.470363 0.468346 0.454905 0.453935 0.453736 0.445692 0.427483 0.340611 0.296213 0.276072 1 0.329387 0.329222 0.325676 0.313547 0.263777 0.229097 0.205477 0.203943 0.191004 0.183465 0.177192 0.169692 2 0.47964 0.479635 0.470631 0.466623 0.455838 0.452361 0.450262 0.439365 0.427224 0.339346 0.297884 0.277743 1

0.463417 0.463398 0.462816 0.462243 0.445706 0.351088 0.290497 0.262871 0.249827 0.213362 0.206615 0.140625 2 0.483835 0.483829 0.480235 0.473582 0.456113 0.42375 0.400682 0.399339 0.394033 0.333022 0.302082 0.281946 1 0.305624 0.300249 0.297605 0.291619 0.271927 0.267062 0.257268 0.229599 0.228729 0.210142 0.203597 0.186679 2 0.493716 0.493712 0.493707 0.479095 0.461743 0.410226 0.379523 0.374971 0.359842 0.311956 0.303628 0.291815 1 0.270121 0.269998 0.268845 0.268003 0.26715 0.266642 0.265523 0.265115 0.257361 0.235734 0.208098 0.20231 2 0.45686 0.433178 0.417522 0.417497 0.411957 0.396301 0.338773 0.337108 0.31287 0.299694 0.280871 0.251024 1

0.341841 0.332605 0.331907 0.314884 0.301673 0.291211 0.227235 0.186504 0.18089 0.178378 0.167916 0.156636 2 0.550403 0.542315 0.502135 0.489164 0.422885 0.39037 0.383 0.379331 0.373219 0.321645 0.296438 0.193393 1 0.304888 0.303232 0.299685 0.298841 0.298541 0.297747 0.280045 0.262089 0.23271 0.219769 0.213906 0.129135 2 0.379446 0.379379 0.372309 0.36445 0.356625 0.356549 0.344781 0.344466 0.326491 0.320851 0.305452 0.279482 1 0.352756 0.351871 0.346848 0.333632 0.290099 0.203637 0.188708 0.171197 0.168484 0.164864 0.158434 0.146323 2 0.470822 0.470815 0.470807 0.46642 0.458017 0.45362 0.449046 0.424136 0.336522 0.289065 0.277286 0.268936 1 0.330959 0.329279 0.327549 0.314055 0.257976 0.228797 0.206379 0.203855 0.1904 0.174043 0.173679 0.172009 2 0.565881 0.555867 0.530725 0.487877 0.387514 0.384117 0.363959 0.27732 0.25845 0.245544 0.205305 0.203652 1 0.293274 0.288713 0.2875 0.280217 0.255961 0.247559 0.234652 0.233762 0.219988 0.197833 0.192931 0.151851 2 0.476084 0.476078 0.476072 0.471277 0.469072 0.454677 0.454515 0.428635 0.342786 0.294325 0.281951 0.274189 1 0.453689 0.453289 0.450073 0.448825 0.448574 0.448374 0.432327 0.343004 0.28075 0.2531 0.238 0.197673 2 Legend: 1(Normal Subject)

2(Glaucoma Subject)

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Appendix K: Testing1, 2 and 3 data used in PNN classification

0.47964 0.479635 0.470631 0.466623 0.455838 0.452361 0.450262 0.439365 0.427224 0.339346 0.297884 0.277743 1

0.483835 0.483829 0.480235 0.473582 0.456113 0.42375 0.400682 0.399339 0.394033 0.333022 0.302082 0.281946 1 0.493716 0.493712 0.493707 0.479095 0.461743 0.410226 0.379523 0.374971 0.359842 0.311956 0.303628 0.291815 1 0.45686 0.433178 0.417522 0.417497 0.411957 0.396301 0.338773 0.337108 0.31287 0.299694 0.280871 0.251024 1

0.550403 0.542315 0.502135 0.489164 0.422885 0.39037 0.383 0.379331 0.373219 0.321645 0.296438 0.193393 1 0.379446 0.379379 0.372309 0.36445 0.356625 0.356549 0.344781 0.344466 0.326491 0.320851 0.305452 0.279482 1 0.470822 0.470815 0.470807 0.46642 0.458017 0.45362 0.449046 0.424136 0.336522 0.289065 0.277286 0.268936 1 0.565881 0.555867 0.530725 0.487877 0.387514 0.384117 0.363959 0.27732 0.25845 0.245544 0.205305 0.203652 1 0.476084 0.476078 0.476072 0.471277 0.469072 0.454677 0.454515 0.428635 0.342786 0.294325 0.281951 0.274189 1 0.267621 0.264991 0.257848 0.257179 0.25717 0.195662 0.172619 0.16594 0.144415 0.126609 0.119355 0.090254 2 0.305624 0.300249 0.297605 0.291619 0.271927 0.267062 0.257268 0.229599 0.228729 0.210142 0.203597 0.186679 2 0.270121 0.269998 0.268845 0.268003 0.26715 0.266642 0.265523 0.265115 0.257361 0.235734 0.208098 0.20231 2 0.341841 0.332605 0.331907 0.314884 0.301673 0.291211 0.227235 0.186504 0.18089 0.178378 0.167916 0.156636 2 0.304888 0.303232 0.299685 0.298841 0.298541 0.297747 0.280045 0.262089 0.23271 0.219769 0.213906 0.129135 2 0.352756 0.351871 0.346848 0.333632 0.290099 0.203637 0.188708 0.171197 0.168484 0.164864 0.158434 0.146323 2 0.330959 0.329279 0.327549 0.314055 0.257976 0.228797 0.206379 0.203855 0.1904 0.174043 0.173679 0.172009 2 0.293274 0.288713 0.2875 0.280217 0.255961 0.247559 0.234652 0.233762 0.219988 0.197833 0.192931 0.151851 2 0.281673 0.272995 0.271313 0.265668 0.239314 0.181397 0.154653 0.154652 0.130285 0.122701 0.120353 0.111443 2

0.364783 0.356189 0.355051 0.33235 0.331402 0.330585 0.328253 0.322958 0.318066 0.298994 0.298993 0.298826 1 0.387624 0.375074 0.372248 0.370004 0.351365 0.351013 0.350392 0.332526 0.319295 0.307433 0.298993 0.298826 1 0.352907 0.332928 0.327812 0.323149 0.315375 0.309897 0.29317 0.287615 0.2812 0.273351 0.272576 0.262788 1 0.356876 0.338486 0.337292 0.33123 0.327561 0.309881 0.300242 0.283176 0.256126 0.22992 0.227039 0.224179 1 0.327228 0.313151 0.312082 0.305742 0.291024 0.253872 0.242057 0.239995 0.235061 0.213745 0.212432 0.211726 1 0.346235 0.328405 0.326265 0.318582 0.291501 0.290052 0.287353 0.281376 0.281101 0.251588 0.240655 0.238283 1 0.311566 0.294595 0.281162 0.280655 0.26433 0.257685 0.252853 0.247276 0.245688 0.223629 0.21615 0.213751 1 0.348318 0.32171 0.312451 0.307687 0.29565 0.294357 0.289028 0.286091 0.278482 0.27335 0.25582 0.241648 1 0.380862 0.373642 0.365609 0.351719 0.351719 0.348821 0.348821 0.348774 0.342991 0.323359 0.315932 0.314018 1 0.315691 0.3149 0.303742 0.298353 0.289143 0.276636 0.273371 0.261364 0.26077 0.237509 0.216965 0.179418 2 0.364783 0.356189 0.355051 0.33235 0.331402 0.330585 0.328253 0.322958 0.318066 0.298994 0.291257 0.269719 2 0.312774 0.31171 0.307347 0.305918 0.304179 0.299061 0.288817 0.283699 0.277141 0.265718 0.260259 0.247221 2 0.283664 0.282687 0.281346 0.275283 0.263511 0.251719 0.238078 0.234968 0.233232 0.136197 0.088421 0.033669 2 0.267621 0.264991 0.257848 0.257179 0.25717 0.195662 0.172619 0.16594 0.144415 0.126609 0.119355 0.090254 2 0.333888 0.327746 0.314763 0.310064 0.276098 0.251526 0.2411 0.213306 0.200041 0.195793 0.182566 0.181782 2 0.281673 0.272995 0.271313 0.265668 0.239314 0.181397 0.154653 0.154652 0.130285 0.122701 0.120353 0.111443 2 0.316231 0.310065 0.308781 0.304577 0.295262 0.210542 0.171471 0.162482 0.160451 0.148983 0.133806 0.114654 2 0.286286 0.284085 0.283093 0.280487 0.270563 0.232888 0.222162 0.213989 0.207064 0.206639 0.202242 0.202218 2

0.387624 0.375074 0.372248 0.370004 0.351365 0.351013 0.350392 0.332526 0.319295 0.307433 0.298993 0.298826 1 0.352907 0.332928 0.327812 0.323149 0.315375 0.309897 0.29317 0.287615 0.2812 0.273351 0.272576 0.262788 1 0.356876 0.338486 0.337292 0.33123 0.327561 0.309881 0.300242 0.283176 0.256126 0.22992 0.227039 0.224179 1 0.327228 0.313151 0.312082 0.305742 0.291024 0.253872 0.242057 0.239995 0.235061 0.213745 0.212432 0.211726 1 0.346235 0.328405 0.326265 0.318582 0.291501 0.290052 0.287353 0.281376 0.281101 0.251588 0.240655 0.238283 1 0.311566 0.294595 0.281162 0.280655 0.26433 0.257685 0.252853 0.247276 0.245688 0.223629 0.21615 0.213751 1 0.348318 0.32171 0.312451 0.307687 0.29565 0.294357 0.289028 0.286091 0.278482 0.27335 0.25582 0.241648 1 0.380862 0.373642 0.365609 0.351719 0.351719 0.348821 0.348821 0.348774 0.342991 0.323359 0.315932 0.314018 1 0.318963 0.30802 0.302889 0.295954 0.294874 0.229322 0.182673 0.160074 0.158654 0.135675 0.113772 0.100011 1 0.364783 0.356189 0.355051 0.33235 0.331402 0.330585 0.328253 0.322958 0.318066 0.298994 0.291257 0.269719 2 0.312774 0.31171 0.307347 0.305918 0.304179 0.299061 0.288817 0.283699 0.277141 0.265718 0.260259 0.247221 2 0.283664 0.282687 0.281346 0.275283 0.263511 0.251719 0.238078 0.234968 0.233232 0.136197 0.088421 0.033669 2 0.267621 0.264991 0.257848 0.257179 0.25717 0.195662 0.172619 0.16594 0.144415 0.126609 0.119355 0.090254 2 0.333888 0.327746 0.314763 0.310064 0.276098 0.251526 0.2411 0.213306 0.200041 0.195793 0.182566 0.181782 2 0.281673 0.272995 0.271313 0.265668 0.239314 0.181397 0.154653 0.154652 0.130285 0.122701 0.120353 0.111443 2 0.316231 0.310065 0.308781 0.304577 0.295262 0.210542 0.171471 0.162482 0.160451 0.148983 0.133806 0.114654 2 0.286286 0.284085 0.283093 0.280487 0.270563 0.232888 0.222162 0.213989 0.207064 0.206639 0.202242 0.202218 2 0.275574 0.274419 0.256336 0.255798 0.250634 0.207682 0.164662 0.137451 0.131172 0.118866 0.062552 0.048399 2 Legend: 1(Normal Subject)

2(Glaucoma Subject)

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Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov

Week 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44

ACTIVITY

1. Project Consideration and Selection

2. Literature Search

2.1 Find relevant reference books and research on IEEE online journals

Duration

2.2 Analyze and study relevant books and journals

3. Preparation of Project Proposal

4. Research on project components

4.1 An introduction to digital image processing using Matlab

4.2 Extraction and compiling of MATLAB codes

5. Digital image Pre-processing using MATLAB

5.1 Familiarization with MATLAB codes

5.2 Extraction and collecting texture features

5.3 Overview of texture features

6. Extraction features using PCA

6.1 Classification with various data mining techniques

6.2 Statistical Analysis

6.3 Glaucoma integrated index

7. Classification result and comparison

8. Preparation of Final Report

8.1 Writing skeleton of final report

8.2 Writing Literature review

8.3 Writing Introduction of report

8.4 Writing Main Body of report

8.5 Writing Conclusion and Further study

8.6 Finalizing and Amendments of reort

9. Preparation of oral presentation

9.1 Review and extract important notes for presentation

9.2 Create poster and prepare for presentation

Legend

Main Task

Sub Task


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Appendix N: MATLAB codes for Fractal Dimension

clc;clear all;close all

I1=imread('image54.bmp');

if length(size(I1))==3

I=rgb2gray(I1);

else

I=I1;

end

[m1 n1]=size(I);

if mod(m1,4)~=0

k=mod(m1,4);

p=zeros(4-k,n1);

I=[I ;p];

m1=size(I,1);

end

if mod(n1,4)~=0

l=mod(n1,4);

p=zeros(m1,4-l);

I=[I p];

end

Imax=double(I);

Imin=double(I);

s=2;

[m n]=size(I);

M=max(m,n);

p=0;

while max(m,n)>2

S=((255*(s/max(m,n))));

Nr=0;

for i=2:2:m-1

sum=0;

for j=2:2:n-1

X=[(Imax(i,j)),(Imax(i,j+1)),(Imax(i+1,j)),(Imax(i+1,j+1))];

Imax(i/2,j/2)=max(X);

Y=[Imin(i,j),Imin(i,j+1),Imin(i+1,j),Imin(i+1,j+1)];

Imin(i/2,j/2)=min(Y);

sum=sum+double((((Imax(i/2,j/2))/S)-((Imin(i/2,j/2))/S)))+1;

end

Nr=Nr+sum;

end

r=s/M;

if Nr

p=p+1;

N(p)=Nr;

r1(p)=r;

D=log(Nr)/log(1/r);

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end

s=s*2;

m=m/2;

n=n/2;

end

plot(log(1./r1),log(N))

xlabel('log(1/r)');

ylabel('log(N)');

X=(log(1./r1)*log(1./r1)');

X1=inv(X);

FD=X1*(log(1./r1))*(log(N)')

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Appendix O: MATLAB codes for Local Binary Patterns

clc;clear all;close all

I1=imread('image1.bmp');

[F]=lbp(I1,2);

function [finalfeaturevector]=lbp(inputimage,uniformitythreshold)

warning off all

img=inputimage;

if isrgb(img)

I=rgb2gray(img);

else

I=img;

end I1=img;

radius=[1 2 3];

noofpts=[8 16 24];

counter=1;

transcount=0;

featurevector=0;

epithelialpixel=find(I>0);

actualpixel=size(epithelialpixel,1);

[M,N]=size(I);

I(M+1,:)=zeros(1,N);

I(:,N+1)=zeros(M+1,1);

for i=1:size(radius,2)

transcount=0;

for j=1+radius(i):size(I,1)-radius(i)

for k=1+radius(i):size(I,2)-radius(i)

if (I(j,k)>0)

ptarray=zeros(1,noofpts(i));

for ptcount=1:noofpts(i)

theta=2*pi*(ptcount-1)/noofpts(i)-pi/2;

vararray(ptcount)=I(round(j+radius(i)*cos(theta)),round(k+radius(i)*si

n(theta)));

end

ptarray(1));

ptarray=sign(vararray-I(j,k));

transition=sum(abs(diff(double(ptarray))));

transition=transition+abs(ptarray(noofpts(i))-

if transition<uniformitythreshold+1

if transition==0

img2(j,k)=2.^(noofpts(i)-1:-

1:0)*double(ptarray');

else

minarray(1)=2.^(noofpts(i)-1:-

1:0)*double(ptarray'); for l=2:noofpts(i)

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1)*ptarray(l-1);

end

minarray(l)=2*minarray(l-1)-(2^noofpts(i)-

end

else

img2(j,k)=min(minarray);

end

transcount=transcount+1;

img2(j,k)=2^noofpts(i);

img3(j,k)=std(double(vararray));

else

img2(j,k)=0;

img3(j,k)=0;

end

end

end

finalfeaturevector(counter)=sum(sum(img3))/actualpixel;

finalfeaturevector(counter+1)=sum(sum(img2))/((2^noofpts(i))*actualpix

el);

imgvar=img2(I1>0);

finalfeaturevector(counter+2)=std(imgvar)/(2^noofpts(i));

counter=counter+3;

end

end

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Appendix P: MATLAB codes for Laws‟ Texture Energy

clc;clear all;close all;

qq=dir;

dat=[];

kk=0;

for i=1:length(qq)

i

if strfind(qq(i,1).name,'.BMP')

filename=(qq(i,1).name);

I1=imread(filename);


I=rgb2gray(I1);

else

I=I1;

end

cd('C:\work\Practice\Group\LTE')

eng_all_img = lawsanalysis(I,3)

fea=[eng_all_img(1,2) eng_all_img(1,3) eng_all_img(2,1)

eng_all_img(2,2) eng_all_img(2,3) eng_all_img(3,1) eng_all_img(3,2)

eng_all_img(3,3)];

dat=[dat ;fea];

end

end

function eng_all_img = lawsanalysis(o,N)

sz = size(o);

mask = lawsmask(N); % COMPUTING MASK USING FUNCTION 'LAWSMASK'.

all_filt = lawsfilter(mask); % COMPUTING ALL FILTERS FOR THE GIVEN

MASK SIZE USING FUNCTION 'LAWSFILTER'.

len = length(mask); % LENGTH, WHICH WILL BE USED AT MANY PLACES.

all_img = lawsimg(double(o),all_filt); % COMPUTING ALL IMAGES BY

APPLYING OBTAINED MASK USING FUNCTION 'LAWSIMG'.

eng_all_img = zeros(len,len); % CELL FOR STORING THE ENERGY.

for m = 1:len

for n = 1:len

new = zeros(sz);

for i=1:sz(1)

for j = 1:sz(2)

new(i,j) = (all_img{m,n}(i,j)^2);

end

end

end

new_sum = sum(new(:));

eng_all_img(m,n) = (new_sum);

end

function out_put = lawsmask(w_s)

L3 = [1 2 1];

E3 = [-1 0 1];

S3 = [-1 2 -1];

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if(w_s==3)

L3 = [1 2 1];

E3 = [-1 0 1];

S3 = [-1 2 -1];

out_put = [L3;E3;S3];

elseif(w_s==5)

L5 = conv(L3,L3);

E5 = conv(L3,E3);

S5 = conv(L3,S3);

W5 = conv(-E3,S3);

R5 = conv(S3,S3);

out_put = [L5;E5;S5;W5;R5];

elseif(w_s==7)

L7 = conv(conv(L3,L3),L3);

E7 = conv(conv(L3,L3),E3);

S7 = conv(conv(L3,L3),S3);

W7 = conv(-(conv(L3,S3)),E3);

R7 = conv(conv(-E3,S3),E3);

O7 = conv(conv(S3,S3),S3);

else

out_put = [L7;E7;S7;W7;R7;O7];

error('invalid mask size, it should be 3,5 or 7 ')

end

function all_filters = lawsfilter(laws_mask)

len = length(laws_mask);

all_filters = cell(len,len);

for i = 1:len

for j = 1:len

all_filters{j,i} = conv2((laws_mask(i,:)),(laws_mask(j,:))');

end

end

function all_img = lawsimg(o_img,all_filter)

sz = size(o_img);

len = length(all_filter);

all_img = cell(len,len);

for m =1:len

for n = 1:len

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mask = all_filter{m,n} ;

temp = double(zeros(sz));

for i = 1:sz(1)

for j = 1:sz(2)

nbr = (expertwin(o_img,i,j,len));

s = sum(mask(:));

if(s==0)

nbr_filt_mul = (mask) .* (nbr);

else

temp(i,j) = double(sum(nbr_filt_mul(:)));

nbr_filt_mul = (mask) .* (nbr);

temp(i,j) = sum(nbr_filt_mul(:))/s;

end

end

end

all_img{m,n} = temp;

end

end

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Appendix Q: MATLAB codes for Fuzzy Gray Level Co-occurrence Matrix

clc;clear all;close all;

pa=cd('E:\DATA\Projects\Wall Data\FG');

qq=dir;

dat=[];

kk=0;

for i=1:length(qq)

i

if strfind(qq(i,1).name,'.bmp')

filename=(qq(i,1).name);

I1=imread(filename);


b=rgb2gray(I1);

else

b=I1;

end

cd('C:\Users\Desktop\Group\FGLCMR')

COM =fuzzycoocc(b); %Call the jcrossCMatrix function for the eye

image

[txttextures] = jtxtAnalyCCMFea(COM); %Calculate the various

features by calling the jtxtAnalyCCMFea

[DifferenceStatistic] = jfDStats(COM); %Calculate Difference

statistic

RLM = jrLengthMat(b, 135, 256);

[RunLength] = jfRLength(RLM);

aatextfeatures=[txttextures DifferenceStatistic RunLength];

dat=[dat ;aatextfeatures];

pa=cd('E:\DATA\Projects\Wall Data\FG');

end

end

function co_mat=fuzzycoocc(ip_img)

ip_img=uint8(ip_img*255);

sz = size(ip_img);

w = 10;

f=zeros(21);

for i=1:21

for j=1:21

f(i,j)=max(0,1-0.1*(abs(i-11)+abs(j-11)));

end

end

a = zeros(256,256);

b = zeros(256,256);

c = zeros(256,256);

d = zeros(256,256);

for i = 1:sz(1)

for j = 1:sz(2)

m = ip_img(i,j);

p=0;

for s=-w:w

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p = p+1;

x = m + s;

if(x>=0 && x<=255)

q=0;

if(i>=1 && j+21<=sz(2))

n = ip_img(i,j+21);

for t = -w:w

q = q+1;

y = n + t;

if(y>=0 && y<=255)

a(x+1,y+1) = a(x+1,y+1)+f(p,q);

end

end

end

end

q=0;

if(i>=1 && j-21>=1)

l = ip_img(i,j-1);

for t = -w:w

q = q + 1;

y = l + t;

if(y>=0 && y<=255)

a(x+1,y+1) = a(x+1,y+1)+f(p,q);

end

end

end

end

% FOR +45 DEGREE.....................................

p=0;

for s=-w:w

p = p + 1;

x = m + s;

if(x>=0 && x<=255)

q=0;

if((i-21)>=1 && (j+21)<=sz(2))

n = ip_img(i,j+21);

for t = -w:w

q = q + 1;

y = n + t;

if(y>=0 && y<=255)

b(x+1,y+1) = b(x+1,y+1)+f(p,q);

end

end

end

end

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q=0;

if((i+21)<=sz(1) && (j-21)>=1)

l = ip_img(i,j-21);

for t = -w:w

q = q + 1;

y = l + t;

if(y>=0 && y<=255)

b(x+1,y+1) = b(x+1,y+1)+f(p,q);


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end

end

end

end

% FOR 90 DEGREE.................................

p=0;

for s=-w:w

p = p + 1;

x = m + s;

if(x>=0 && x<=255)

q=0;

if((i-21)>=1 && j>=1)

n = ip_img(i-21,j);

for t = -w:w

q = q + 1;

y = n + t;

if(y>=0 && y<=255)

c(x+1,y+1) = c(x+1,y+1)+f(p,q);

end

end

end

end

q=0;

if((i+21)<=sz(1) && j>=1)

l = ip_img(i+21,j);

for t = -w:w

q = q + 1;

y = l + t;

if(y>=0 && y<=255)

c(x+1,y+1) = c(x+1,y+1)+f(p,q);

end

end

end

end

% FOR -45 DEGREE..................................

p=0;

for s=-w:w

p = p + 1;

x = m + s;

if(x>=0 && x<=255)

q = 0;

if((i-21)>=1 && (j-21)>=1)

n = ip_img(i-21,j-21);

for t = -w:w

q = q + 1;

y = n + t;

if(y>=0 && y<=255)

d(x+1,y+1) = d(x+1,y+1)+f(p,q);

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end

end

end

end


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q=0;

if((i+21)<=sz(1) && (j+21)<=sz(2))

l = ip_img(i+21,j+21);

for t = -w:w

q = q + 1;

y = l + t;

if(y>=0 && y<=255)

d(x+1,y+1) = d(x+1,y+1)+f(p,q);

end

end

end

end

end

end

co_mat = round((1/4)*((a) + (b) + (c) + (d)));

function [txttextures] = jtxtAnalyCCMFea(M)

% Features extracted from Cross Cooccurrence Matrix

Ho = 0; En = 0; Co = 0; Sy = 0; Ent = 0; Cor = 0;

m1 = 0; m2 = 0; m3 = 0; m4 = 0; Pdx(256) = 0; Pdy(256) = 0;

MS = sum(sum(M));

for i_0 = 1:size(M, 1)

for j_0 = 1:size(M, 2)

Pdx(i_0) = Pdx(i_0) + M(i_0, j_0);

Pdy(j_0) = Pdy(j_0) + M(i_0, j_0);

end

end

miu_x = mean(Pdx); miu_y = mean(Pdy); sig_x = std(Pdx); sig_y =

std(Pdy);

for i = 1:size(M, 1)

for j = 1:size(M, 2)

Ho = Ho + (M(i, j)/(1 + abs(i - j)))/MS;

En = En + (M(i, j)*M(i, j))/MS;

Co = Co + (abs(i - j)*M(i, j))/MS;

Sy = Sy + (abs(M(i, j)- M(j, i)))/MS;

Cor = Cor + (i - miu_x)*(j - miu_y)*M(i, j)/(sig_x*sig_y*MS);

m1 = m1 + ((i - j)*M(i, j))/MS;

m2 = m2 + (((i - j)^2)*M(i, j))/MS;

m3 = m3 + (((i - j)^3)*M(i, j))/MS;

m4 = m4 + (((i - j)^4)*M(i, j))/MS;

if M(i, j) ~= 0

Ent = Ent - (M(i,j)/MS)*log10(M(i,j)/MS);

end

end

end

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Sy = 1 - Sy;

nametxttextures=['Homogeneity ', 'Energy ', 'Entropy ', 'Contrast ',

'Symmetry ' , 'Correlation ','Moment1 ','Moment2 ','Moment3 ','Moment4

'];

txttextures = [Ho,En,Ent,Co,Sy,Cor,m1,m2,m3

function [DifferenceStatistic] = jfDStats(M)

% M has to be a co-occurrence matrix

dVEC(size(M, 1))= 0;

aSM = 0; cON = 0; eNT = 0; mEAN = 0;

lvl = (size(M, 1)^2);



dVEC(abs(i - j) + 1) = dVEC(abs(i - j) + 1) + double(M(i, j));

end

end

clear i


aSM = aSM + (dVEC(i)^2)/lvl;

cON = cON + ((i - 1)^2)*dVEC(i)/lvl;

mEAN = mEAN + (i - 1)*dVEC(i)/lvl;

if ~(dVEC(i) == 0)

eNT = eNT - (dVEC(i)/lvl)*log10(dVEC(i)/lvl);

end

end

DifferenceStatistic=[aSM, cON, mEAN, eNT];

nameDifferenceStatistic=['Angular2ndMoment ','Contrast ','Mean ',

'Entropy '];

function [rM EM] = jrLengthMat(M, deg, lvl)

S1 = size(M, 1); S2 = size(M, 2);

mARK = NaN; tIMES = 0; lOOP = 1; i = 0;

if (min(min(M)) == 0) % turn values 0~X to 1~(X + 1)

M = M + 1;

end

if (deg == 0)

EM = transpose(M);

EM = EM(:)';

sVEC(S2) = 0;

rM = zeros(lvl, S2);

while (lOOP <= ((size(EM, 2)/size(M, 2))))

sVEC = EM(((lOOP - 1)*S2 + 1):((lOOP - 1)*S2 + S2));

i = 1;

while (i <= S2)

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if (i == 1)

mARK = sVEC(i);

tIMES = 1;

i = i + 1;

elseif ((i > 1) && (i < S2))

if (sVEC(i) == mARK)

tIMES = tIMES + 1;

elseif (~(sVEC(i) == mARK))

rM(mARK, tIMES) = rM(mARK, tIMES) + 1;

tIMES = 1;

mARK = sVEC(i);

end

i = i + 1;

elseif (i == S2)


tIMES = tIMES + 1;




tIMES = 1;

mARK = sVEC(i);


end

end

end

i = i + 1;

end

lOOP = lOOP + 1;

elseif (deg == 90)

EM = M;

EM = EM(:)';

sVEC(S1) = 0;


while (lOOP <= ((size(EM, 2)/size(M, 1))))


i = 1;

while (i <= S1)

if (i == 1)

mARK = sVEC(i);

tIMES = 1;

i = i + 1;

elseif ((i > 1) && (i < S1))


tIMES = tIMES + 1;



tIMES = 1;

mARK = sVEC(i);

end

i = i + 1;

elseif (i == S1)


tIMES = tIMES + 1;


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tIMES = 1;

mARK = sVEC(i);


end

end

end

i = i + 1;

end

lOOP = lOOP + 1;

elseif (deg == 45)

if (S2 >= S1)

BM = [M M];

EM = zeros(S2, S1);

dVEC(S1) = 0;

for dj = 1:S2

for di = 1:S1

dVEC(di) = BM(di, (dj + di - 1));

end

end

EM(dj, :) = dVEC;

elseif (S1 > S2)

BM = [M; M];

EM = zeros(S1, S2);

dVEC(S2) = 0;

for di = 1:S1

for dj = 1:S2

dVEC(dj) = BM((di + dj - 1), dj);

end

EM(di, :) = dVEC;

end

end

S2 = size(EM, 2);

EM = transpose(EM);

EM = EM(:)';

sVEC(S2) = 0;


while (lOOP <= ((size(EM, 2)/S2)))


i = 1;

while (i <= S2)

if (i == 1)

mARK = sVEC(i);

tIMES = 1;

i = i + 1;

elseif ((i > 1) && (i < S2))


tIMES = tIMES + 1;



tIMES = 1;

mARK = sVEC(i);

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end

i = i + 1;

elseif (i == S2)


tIMES = tIMES + 1;




tIMES = 1;

mARK = sVEC(i);


end

end

end

i = i + 1;

end

lOOP = lOOP + 1;

elseif (deg == 135)

M = fliplr(M);

if (S2 >= S1)

BM = [M M];

EM = zeros(S2, S1);

dVEC(S1) = 0;

for dj = 1:S2

for di = 1:S1

dVEC(di) = BM(di, (dj + di - 1));

end

else

end

EM(dj, :) = dVEC;

BM = [M; M];

EM = zeros(S1, S2);

dVEC(S2) = 0;

for di = 1:S1

for dj = 1:S2

dVEC(dj) = BM((di + dj - 1), dj);

end

end

end

EM(di, :) = dVEC;

S2 = size(EM, 2);

EM = transpose(EM);

EM = EM(:)';

sVEC(S2) = 0;


while (lOOP <= ((size(EM, 2)/S2)))


i = 1;

while (i <= S2)

if (i == 1)

mARK = sVEC(i);

tIMES = 1;

i = i + 1;

elseif ((i > 1) && (i < S2))


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tIMES = tIMES + 1;



tIMES = 1;

mARK = sVEC(i);

end

i = i + 1;

elseif (i == S2)


tIMES = tIMES + 1;




tIMES = 1;

mARK = sVEC(i);


end

end

end

i = i + 1;

end

lOOP = lOOP + 1;

end

function [RunLength] = jfRLength(M)

SM = sum(sum(M)); sREMP = 0; lREMP = 0;

gLNUni = sum(sum(M).*sum(M))/SM;

rLNUni = sum(sum(transpose(M)).*sum(transpose(M)))/SM;

rPER = SM/(size(M, 1)*(size(M, 2)));



sREMP = sREMP + M(i, j)/(j^2);

lREMP = lREMP + M(i, j)*(j^2);

end

end

sREMP = sREMP/SM; lREMP = lREMP/SM;

RunLength=[sREMP, lREMP, rPER, gLNUni, rLNUni];

nameRunLength=['ShortRunEmphasis ', 'LongRunEmphasis ', 'RunPer ',

'GrayLvlNonUni ', 'RunLengthNonUni '];

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Appendix R: MATLAB codes for PCA

x=Data;

numdata=33;% specifying the data set number

xmean=mean(x)% to find the mean

xnew=x-xmean*ones(numdata,1)% same as finding the scores

covariancematrix=cov(xnew)% to find covariance

[V,D]=eig(covariancematrix)%to find the eigenvalues and eigenvector

D=diag(D)

finaldata=V'*[xnew]'% this gives the final principle components

PCA1=unique(finaldata)% to remove the duplicates

PCA1(PCA1<0)=[]% to remove the negative values

PCA1=sort(PCA1,'descend')%sorting the new vector in descending order

FinalPCA=PCA1(1:12)%selected the largest 12 PCA components

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Appendix S: MATLAB codes for PNN Classifier

clc

close all;

clear all;

for pp=1:3

filename1=['Testing',num2str(pp),'.mat'];

filename2=['net',num2str(pp),'.mat'];

load(filename1)

load(filename2)

P = Testing(:,[1,2,3,4,5,6,7,8,9,10,11,12]);

P=P'

Y = sim(net,P);

y = vec2ind(Y)

normwrong=0;

normright=0;

glauright=0;

glauwrong=0;

for i=10:18

if y(1,i)==2

glauright=glauright+1;

else

end

glauwrong=glauwrong+1;

end

for i=1:9

if y(1,i)==1

normright=normright+1;

else

normwrong=normwrong+1;

end

end

str1=num2str(normright);

str2=num2str(normwrong);

str7=num2str(glauright);

str8=num2str(glauwrong);

ans=(glauright+normright)/21*100;

str11=([num2str(ans,'%.1f')]);

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TN=normright;

FP=normwrong;

TP=glauright;

FN=glauwrong;

sensi=(TP/(TP+FN))*100

speci=(TN/(TN+FP))*100

ppv=(TP/(TP+FP))*100

final(pp,:)=[TN;FN;TP;FP;ans;ppv;sensi;speci]'

end

final(4,1)=round(mean(final(1:3,1)));




final(4,5)=mean(final(1:3,5));

a=([num2str(final(4,5),'%.1f')]);

final(4,5)=str2num(a);


b=([num2str(final(4,6),'%.1f')]);

final(4,6)=str2num(b);


c=([num2str(final(4,7),'%.1f')]);

final(4,7)=str2num(c);


d=([num2str(final(4,8),'%.1f')]);

final(4,8)=str2num(d);

clc;close all;clear all;

for pp=1:3

filename1=['Training',num2str(pp),'.mat']

filename2=['net',num2str(pp),'.mat']

load(filename1)

P = Training(:,[1,2,3,4,5,6,7,8,9,10,11,12]);

P=P';

class=Training(:,13);

T = ind2vec(class);

net = newpnn(P,T,0.015);

save(filename2,'net')

end

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automated glaucoma detection

Education

detection of glaucoma

glaucoma eye anatomy

normal eye fundus image

analysis of glaucoma

thediagnosis of glaucoma

glaucoma subjects

b fundus image

b glaucoma fd