AUTOMATIC TUMOR DETECTION ANDCLASSIFICATION OF BRAIN IMAGE

A PROJECT REPORT

Submitted by

KALIDAS.U 72306106019

KANIMOZHI.K 72306106020

KANIMOZHI.T 72306106021

RAJESH.V 72306106046

In partial fulfillment for the award of the degreeof

BACHELOR OF ENGINEERING

in

ELECTRONICS AND COMMUNICATION

ENGINEERING

of

ANNA UNIVERSITY, CHENNAI – 600 025

DEPARTMENT OF ELECTRONICS AND COMMUNICATION

ENGINEERING

VELALAR COLLEGE OF ENGINEERING AND TECHNOLOGY

ERODE-638 012.

APRIL 2010

VELALAR COLLEGE OF ENGINEERING ANDTECHNOLOGY, ERODE- 9.

DEPARTMENT OF ELECTRONICS ANDCOMMUNICATION ENGINEERING

1

Certificate

2

BONAFIDE CERTIFICATE

This is to certify that, the project report titled “AUTOMATIC TUMOR

DETECTION AND CLASSIFICATION OF BRAIN IMAGE” is the

bonafide work of

KALIDAS.U 72306106019

KANIMOZHI.K 72306106020

KANIMOZHI.T 72306106021

RAJESH.V 72306106046

Submitted in partial fulfillment of the requirements for the degree of

BACHELOR OF ENGINEERING during the year 2006-2010.

Dr.K.VENKATACHALAM, M.Tech., Ph.D., Mrs. J.NANDHINI B.E.,

HEAD OF THE DEPARTMENT SUPERVISOR & LECTURER

DEPARTMENT OF ECE DEPARTMENT OF ECE

Submitted for the university examination held on 08.04.2010 & 09.04.2010

INTERNAL EXAMINER EXTERNAL EXAMINER

3

Acknowledgement

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ACKNOWLEDGEMENT

We are privileged to express our heartfelt thanks to our honorable

secretary Mr.S.D.CHANDRASEKAR B.A., who provided all the

facilities to build our project.

We hereby thank our former Principal and Administrative Director

Dr. P. SABAPATHI B.E. (Hons.), M.Sc., (Engg.), Ph.D., and our

Principal Dr. K.PALANISWAMY, M.E., Ph.D., who have been a great

inspiration not only for this project but also throughout this course of

study.

We express our profound gratitude to our beloved Head of the

Department Dr. K. VENKATACHALAM, M.Tech., (PhD) who

laconically brought us to the processor world.

We are highly indebted to our gregarious guide

Mrs.J.NANDHINI for her valuable guidance, advice and helps rendered

whenever we approached her in times of need.

We express our sincere thanks to our project coordinator

Dr.T.BALAKUMARAN,M.E., Phd., for their guidance to complete our

project successfully.

We are also highly thankful to all our indefatigable staff members

and non teaching staffs for helping us throughout the completion of the

project.

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Abstract

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AUTOMATIC TUMOR DETECTION AND CLASSIFICATION OF BRAIN IMAGE

ABSTRACT:

Segmentation of anatomical regions of the brain is the fundamental

problem in medical image analysis. In this paper, a brain tumor

segmentation method has been developed and validated segmentation on

2D MRI Data. This method can segment a tumor provided that the desired

parameters are set properly. This method does not require any

initialization while the others require an initialization inside the tumor. In

our segmentation approach watershed segmentation algorithm is used.

Watershed uses the intensity as a parameter to segment the whole image

data set. The input MRI image is preprocessed and loaded into matlab

workspace. In the segmentation process the image is divided into blocks

depending on the edge, gray and threshold parameter. The blocks are

divided by comparing the intensity value of the image with the parameters

as the intensity of the tumor affected area will be higher. Likewise the

tumor surface from the MRI image is segmented out. After the detection

of the tumor it is then classified using ICA algorithm which gives the type

of the tumor for the doctor’s convenience. Here the threshold limit is

applied to each image and the limit is tested on the ICA applied

algorithm. According to the intensity, tumor is classified into

ASTROCYTOMA, GLIOBLASTOMA, LYMPHOMA,

MENINGLOMA

7

i

8

Table of contents

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TABLE OF CONTENT

CHAPTE

R

NO

TITLE

PAG

E NO

ABSTRACT i

LIST OF FIGURES v

LIST OF ABBREVATION vi

1. INTRODUCTION 1

2. LITERATURE REVIEW 4

2.1 IMAGING TECHNIQUES 4

2.1.1 Electron microscopy 4

2.1.2 Fluoroscopy 5

2.1.3 X Rays 5

2.1.3.1 Projection radiography 5

2.1.3.2 Computer tomography 6

2.1.3.3 Angiogram 8

2.1.4 Mammography 9

2.1.5 Magnetic Resonance Imaging 11

2.1.6 Ultrasonography 12

2.1.7 Thermography 13

2.1.8 Positron Emission Tomography 14

2.1.9 Photo Acoustic Imaging 14

2.1.10 Endoscopic Imaging 15

2.2 BRAIN TUMOR AND STAGES 15

2.2.1 Introduction 16

2.2.2 Stages of tumor 17

2.3 CAUSES OF BRAIN TUMOR 17

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2.3.1 Race 17

2.3.2 Age 17

2.3.3 Family history 18

2.4 SYMPTOMPS OF BRAIN TUMOR 18

2.4.1 Head ache 18

2.4.2Seizures 18

2.4.3 Nausea and vomiting 19

2.4.4 Behavioural and cognitive Problems 19

2.5 TESTS AND DIAGNOSIS 19

2.5.1 A Neurological exam 19

2.5.2 Imaging test 19

2.5.3 Biopsy 20

2.6 TYPES OF TUMOR 20

2.6.1 Acoustic Neuroma 20

2.6.2 Astrocytom 21

2.6.2.1 Pilocytic Astrocytoma 21

2.6.2.2 Low-grade Astrocytoma 21

2.6.2.3 Anaplastic Astrocytoma 22

2.6.2.4 Anaplastic Astrocytoma 22

2.6.3 Glioblastoma multiframe 23

2.6.4 Chordoma 23

2.6.5 CNS Lymphoma 24

2.6.6 Craniopharyngioma 25

2.6.7 Brain stem Glioma 26

2.6.8 Meningioma 27

2.6.9 Schwannoma 29

2.6.10 Ependymoma 29

2.6.11 Rhabdoid tumor 31

11

ii

iii

3. SEGMENTATION ALGORITHMS 32

3.1 EDGE DETECTION 32

3.1.1 Sobel operator 32

3.1.2 Canny operator 35

3.1.3 Prewitt’s operator 36

3.1.4 Robertt’s cross operator 38

3.2 HISTOGRAM EQUALIZATION 40

3.3 THRESHOLDING TECHNIQUES 42

3.4 REGION BASD SEGMENTATION 44

3.5 FUZZY C-MEANS ALGORITHM 45

4. PROJECT DESCRIPTION 50

4.1 BLOCK DIAGRAM 50

4.2 WATERSHED SEGMENTATION 50

4.3 INDEPENDENT COMPONENT

ANALYSIS

55

4.3.1 Introduction 55

4.3.1.1 Linear noiseless ICA 56

4.3.2 Need for classification 57

4.3.3 Preprocessing steps in ICA 60

4.3.3.1 Centering 60

4.3.3.2 Whitening 60

4.4 COMPARISION OF PCA AND ICA 62

5. RESULT 63

6. CONCLUSION 65

APPENDIX 66

7. REFERENCE 71

12iv

List of figures

13

LIST OF FIGURES

FIGURE

NOTITLE PAGENO

2.1 Computer Tomography 7

3.1 Original Brain MR Image 34

3.2 Output of Edge Detection by Sobel Operator 34

3.3 Output of Edge Detection by Canny Operator 36

3.4 Output of Edge Detection by Prewitt Operator 38

3.5 Output of Edge Detection by Roberts Operator

40

3.6 Histogram 41

3.7 Output of Histogram Equalized Image 41

3.8 Output for Various Threshold Values 43

3.9 Output of Region Based Segmentation 45

3.10 Output of FCM Algorithm 49

4.1 Block Diagram 50

4.2 Segmentation using Watershed Algorithm 51

4.3 Original MR Image 53

4.4 Enhanced Image 53

4.5 Boundary Extraction of Reconstructed Image 54

4.6 Boundary Super Imposed on Original Image 54

4.7 Block Diagram of Spatial and Temporal ICA 58

4.8 Plot of ICA and PCA 62

14v

LIST OF ABBREVATIONS

15

LIST OF ABBREVIATIONS

MRI MAGNETIC RESONANCE IMAGING

CT COMPUTER TOMOGRAPHY

OPT ORTHOPANTOMOGRAPHY

NDE NONDESTRUCTIVE EVALUATION

DSA DIGITAL SUBTRACTION ANGIOGRAPHY

BSE BREAST SELF-EXAMINATION

PEM POSITRON EMISSION MAMMOGRAPHY

NMRI NUCLEAR MAGNETIC RESONANCE IMAGING

OCT OPTICAL COHERENCE TOMOGRAPHY

PNET PRIMITIVE NEUROECTODERMAL TUMOR

FCM FUZZY C-MEANS

SICA SPATIAL INDEPENDENT COMPONENT ANALYSIS

TICA TEMPORAL INDEPENDENT COMPONENT ANALYSIS

16vi

Chapter-1Introduction

17

CHAPTER 1

INTRODUCTION

The body is made up of many types of cells. Each

type of cell has special functions. Most cells in the body

grow and then divide in an orderly way to form new cells

as they are needed to keep the body healthy and working

properly. When cells lose the ability to control their

growth, they divide too often and without any order. The

extra cells form a mass of tissue called a tumor. Tumors

are benign or malignant. The aim of this work is to design

an automated tool for brain tumor quantification using

MRI image data sets. Magnetic Resonance Imaging (MRI) is the

state of the art medical imaging technology which allows cross sectional

view of the body with unprecedented tissue contrast. MRI plays an

important role in assessing pathological conditions of the ankle, foot and

brain. It has rapidly evolved into an accepted modality for medical

imaging of disease processes in the musculoskeletal system, especially

the foot and brain due to the use of non-ionizing radiation.

MRI provides a digital representation of tissue characteristic that

can be obtained in any tissue plane. The images produced by an MRI

scanner are best described as slices through the brain. MRI has the added

advantage of being able to produce images which slice through the brain

in both horizontal and vertical planes. This work is a small and

modest part of a quite complex system. The whole system

when completed visualizing the inside of the human body,

it makes surgeons able to perform operations inside a

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patient without open surgery. More specifically the aim for

this work is to segment a tumor in a brain. This will make

the surgeon able to see the tumor and then ease the

treatment. The instruments needed for this could be

ultrasound, Computer Tomography (CT scan) and

Magnetic Resonance Imaging (MRI). In this Paper, the

technique used is Magnetic Resonance Imaging (MRI). The

segmentation of brain tumors in magnetic resonance images (MRI) is a

challenging and difficult task because of the variety of their possible

shapes, locations, image intensities.

Segmentation is an important process to extract information from

complex medical images. Segmentation has wide application in medical

field. The main objective of the image segmentation is to partition an

image into mutually exclusive and exhausted regions such that each

region of interest is spatially contiguous and the pixels within the region

are homogeneous with respect to a predefined criterion. Widely used

homogeneity criteria include values of intensity, texture, color, range,

surface normal and surface curvatures. Here Watershed

segmentation based algorithm has been used for

detection of tumor. Watershed segmentation uses the

intensity as a parameter to segment the whole image

data set. Moreover, the additional complexity of

estimation imposed to other algorithms causes a tendency

towards density dependent approaches. Among all

possible methods for this purpose, watershed can be used

as a powerful tool which implicitly extracts the tumor

surface. For detection of tumor and its classification in 2D

the software used is MATLAB.

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After the segmentation of the detected tumor, the

classification is applied to the segmented surface. The

algorithm used here for the classification is ICA.

Independent component analysis (ICA) which has recently been

developed in the area of image processing. ICA is a variant of principal

component analysis (PCA) in which the components are assumed to be

mutually statistically independent instead of merely uncorrelated. The

stronger condition allows one to remove the rotational invariance of

PCA, i.e. ICA provides a meaningful unique bilinear decomposition of

two-way data that can be considered as a linear mixture of a number of

independent source signals. On applying the ICA algorithm to the

segmented tumor it is classified that, if the intensity found is between 248

and 256 , it is found to be ASTROCYTOMA and for the values between

224 and 228 it is found to be GLIOBLASTOMA. For the values between

238 and 240 it is found to be LYMPHOMA and for values between 263

and 290 it is found to be MENINGLOMA.

This report consists of six chapters. The second chapter provides a

brief insight about the medical imaging techniques commercially

available. The third chapter explains about the development of brain

tumor and its types. The fourth chapter gives a literature survey of

various segmentation algorithms available for brain MRI image. The fifth

chapter gives a brief description about this project and its corresponding

results and the sixth chapter leads to the conclusion.

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Chapter-2Literature review

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CHAPTER 2

MEDICAL IMAGING TECHNIQUES

2.1.1 ELECTRON MICROSCOPY

An Electron Microscope is a type of microscope that uses a

particle beam of electrons to illuminate a specimen and create a highly-

magnified image. Electron microscopes have much greater resolving

power than light microscopes that use electromagnetic radiation and can

obtain much higher magnifications of up to 2 million times, while the

best light microscopes are limited to magnifications of 2000 times. Both

electron and light microscopes have resolution limitations, imposed by

the wavelength of the radiation they use. The greater resolution and

magnification of the electron microscope is because the wavelength of an

electron; its de Broglie wavelength is much smaller than that of a photon

of visible light.

The electron microscope uses electrostatic and electromagnetic

lenses in forming the image by controlling the electron beam to focus it

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at a specific plane relative to the specimen. This manner is similar to how

a light microscope uses glass lenses to focus light on or through a

specimen to form an image.

Types of Electron

a) Transmission Electron Microscope (TEM)

b) Scanning Electron Microscope (SEM)

c) Reflection Electron Microscope (REM)

d) Scanning Transmission Electron Microscope (STEM)

2.1.2 FLUOROSCOPY

Fluoroscopy is an imaging technique commonly used by

physicians to obtain real-time moving images of the internal structures of

a patient through the use of a fluoroscope. In its simplest form, a

fluoroscope consists of an x-ray source and fluorescent screen between

which a patient is placed. However, modern fluoroscopes couple the

screen to an x-ray image intensifier and CCD video camera allowing the

images to be recorded and played on a monitor.

The first fluoroscopes consisted of an x-ray source and fluorescent

screen between which the patient would be placed. As the x rays pass

through the patient, they are attenuated by varying amounts as they

interact with the different internal structures of the body, casting a

shadow of the structures on the fluorescent screen. Images on the screen

are produced as the untenanted X rays interact with atoms in the screen

through the photoelectric effect, giving their energy to the electrons.

While much of the energy given to the electrons is dissipated as heat, a

fraction of it is given off as visible light, producing the images. Early

23

radiologists would adapt their eyes to view the dim fluoroscopic images

by sitting in darkened rooms, or by wearing red adaptation goggles.

2.1.3 X- RAYS

2.1.3.1 PROJECTION RADIOGRAPHY

Radiographs, more commonly known as x-rays, are often used to

determine the type and extent of a fracture as well as for detecting

pathological changes in the lungs. With the use of radio-opaque contrast

media, such as barium, they can also be used to visualize the structure of

the stomach and intestines - this can help diagnose ulcers or certain types

of colon cancer.

2.1.3.2 COMPUTED TOMOGRAPHY

Tomography is the method of imaging a single plane, or slice, of

an object resulting in a tomogram. There are several forms of

tomography:

Linear tomography

Poly tomography

Zonography

Orthopantomography (OPT or OPG)

Computed Tomography (CT), or Computed Axial Tomography

A basic problem in imaging with x-rays (or other penetrating

radiation) is that a two-dimensional image is obtained of a three-

dimensional object. This means that structures can overlap in the final

image, even though they are completely separate in the object. This is

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particularly troublesome in medical diagnosis where there are many

anatomic structures that can interfere with what the physician is trying to

see. During the 1930's, this problem was attacked by moving the x-ray

source and detector in a coordinated motion during image formation.

From the geometry of this motion, a single plane within the patient

remains in focus, while structures outside this plane become blurred. This

is analogous to a camera being focused on an object at 5 feet, while

objects at a distance of 1 and 50 feet are blurry. These related techniques

based on motion blurring are now collectively called classical

tomography. The word tomography means "a picture of a plane". In spite

of being well developed for more than 50 years, classical tomography is

rarely used. This is because it has a significant limitation: the interfering

objects are not removed from the image, only blurred. The resulting

image quality is usually too poor to be of practical use. The long sought

solution was a system that could create an image representing a 2D slice

through a 3D object with no interference from other structures in the 3D

object. This problem was solved in the early 1970s with the introduction

of a technique called computed tomography (CT). Computed

Tomography (CT) is a powerful nondestructive evaluation (NDE)

technique for producing 2-D and 3-D cross-sectional images of an object

from flat X-ray images. Figure 2.1 shown below is a schematic of a CT

system.

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Figure 2.1 Computed Tomography

26

Characteristics of the internal structure of an object such as

dimensions, shape, internal defects, and density are readily available

from CT images. The test component is placed on a turntable stage that is

between a radiation source and an imaging system. The turntable and the

imaging system are connected to a computer so that x-ray images

collected can be correlated to the position of the test component. The

imaging system produces a 2-dimensional shadowgraph image of the

specimen just like a film radiograph.

2.1.3.3 ANGIOGRAPHY

Angiography or Arteriography is a medical imaging technique

used to visualize the inside, or lumen, of blood vessels and organs of the

body, with particular interest in the arteries, veins and the heart

chambers. This is traditionally done by injecting a radio-opaque contrast

agent into the blood vessel and imaging using X-ray based techniques

such as fluoroscopy. The word itself comes from the Greek words

angeion, "vessel", and graphein, "to write or record". The film or image

of the blood vessels is called an angiograph, or more commonly, an

angiogram.

Although the term angiography is strictly defined as based on

projectional radiography, the term has been applied to newer vascular

imaging techniques such as CT angiography and MR angiography.

Depending on the type of angiogram, access to the blood vessels is

gained most commonly through the femoral artery, to look at the left side

of the heart and the arterial system or the jugular or femoral vein, to look

at the right side of the heart and the venous system. Using a system of

guide wires and catheters, a type of contrast agent (which shows up by

27

absorbing the x-rays), is added to the blood to make it visible on the x-

ray images.

The X-ray images taken may either be still images, displayed on a

image intensifier or film, or motion images. For all structures except the

heart, the images are usually taken using a technique called digital

subtraction angiography (DSA). Images in this case are usually taken at 2

- 3 frames per second, which allows the radiologist to evaluate the flow

of the blood through a vessel or vessels. This technique "subtracts" the

bones and other organs so only the vessels filled with contrast agent can

be seen. The heart images are taken at 15-30 frames per second, not using

a subtraction technique. Because DSA requires the patient to remain

motionless, it cannot be used on the heart. Both these techniques enable

the radiologist or cardiologist to see stenosis (blockages or narrowings)

inside the vessel which may be inhibiting the flow of blood and causing

pain.

2.1.4 MAMMOGRAPHY

Mammography is the process of using low-dose amplitude-X-rays

(usually around 0.7 mSv) to examine the human breast and is used as a

diagnostic as well as a screening tool. The goal of mammography is the

early detection of breast cancer, typically through detection of

characteristic masses and/or microcalcifications. Mammography is

believed to reduce mortality from breast cancer. No other imaging

technique has been shown to reduce risk, but breast self-examination

(BSE) and physician examination are considered essential parts of

regular breast care.

In many countries routine mammography of older women is

encouraged as a screening method to diagnose early breast cancer. The

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United States Preventive Services Task Force recommends screening

mammography, with or without clinical breast examination, every 1-2

years for women aged 40 and older. Altogether clinical trials have found

a relative reduction in breast cancer mortality of 20%, but the two

highest-quality trials found no reduction in mortality. Mammograms have

been controversial since 2000, when a paper highlighting the results of

the two highest-quality studies was published. Normally longer

wavelength X-rays are used for taking mammograms. Radiologists then

analyze the image for any abnormal findings.

At this time, mammography along with physical breast

examination is the modality of choice for screening for early breast

cancer. Ultrasound, ductography, positron emission mammography

(PEM), and magnetic resonance imaging are adjuncts to mammography.

Ultrasound is typically used for further evaluation of masses found on

mammography or palpable masses not seen on mammograms.

Ductograms are still used in some institutions for evaluation of bloody

nipple discharge when the mammogram is non-diagnostic. MRI can be

useful for further evaluation of questionable findings as well as for

screening pre-surgical evaluation in patients with known breast cancer to

detect any additional lesions that might change the surgical approach, for

instance from breast-conserving lumpectomy to mastectomy. New

procedures, not yet approved for use in the general public, including

breast tomosynthesis may offer benefits in years to come.

Mammography has a false-negative (missed cancer) rate of at least

10 percent. This is partly due to dense tissues obscuring the cancer and

the fact that the appearance of cancer on mammograms has a large

overlap with the appearance of normal tissues.

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2.1.5 MAGNETIC RESONANCE IMAGING (MRI)

MRI or nuclear magnetic resonance imaging (NMRI), is primarily

a medical imaging technique most commonly used in radiology to

visualize the internal structure and function of the body. MRI provides

much greater contrast between the different soft tissues of the body than

computed tomography (CT) does, making it especially useful in

neurological (brain), musculoskeletal, cardiovascular, and oncological

(cancer) imaging. Unlike CT, it uses no ionizing radiation, but uses a

powerful magnetic field to align the nuclear magnetization of (usually)

hydrogen atoms in water in the body. Radio frequency (RF) fields are

used to systematically alter the alignment of this magnetization, causing

the hydrogen nuclei to produce a rotating magnetic field detectable by the

scanner. This signal can be manipulated by additional magnetic fields to

build up enough information to construct an image of the body.

How MRI works

The body is largely composed of water molecules which each

contain two hydrogen nuclei or protons. When a person goes inside the

powerful magnetic field of the scanner, these protons align with the

direction of the field.

A radio frequency electromagnetic field is then briefly turned on,

causing the protons to absorb some of its energy. When this field is

turned off the protons release this energy at a resonance radio frequency

which can be detected by the scanner. The frequency of the emitted

signal depends on the strength of the magnetic field. The position of

protons in the body can be determined by applying additional magnetic

fields during the scan which allows an image of the body to be built up.

30

These are created by turning gradients coils on and off which creates the

knocking sounds heard during an MR scan.

Diseased tissue, such as tumors, can be detected because the

protons in different tissues return to their equilibrium state at different

rates. By changing the parameters on the scanner this effect is used to

create contrast between different types of body tissue. MRI is used to

image every part of the body, and is particularly useful for neurological

conditions, for disorders of the muscles and joints, for evaluating tumors,

and for showing abnormalities in the heart and blood vessels.

2.1.6 ULTRASONOGRAPHY

Medical ultrasonography uses high frequency broadband sound

waves in the megahertz range that are reflected by tissue to varying

degrees to produce (up to 3D) images. This is commonly associated with

imaging the fetus in pregnant women. Uses of ultrasound are much

broader, however. Other important uses include imaging the abdominal

organs, heart, breast, muscles, tendons, arteries and veins. While it may

provide less anatomical detail than techniques such as CT or MRI, it has

several advantages which make it ideal in numerous situations, in

particular that it studies the function of moving structures in real-time,

emits no ionizing radiation, and contains speckle that can be used in

electrograph. It is very safe to use and does not appear to cause any

adverse effects, although information on this is not well documented. It is

also relatively inexpensive and quick to perform. The real time moving

image obtained can be used to guide drainage and biopsy procedures.

Doppler capabilities on modern scanners allow the blood flow in arteries

and veins to be assessed.

31

2.1.7 THERMOGRAPHY

Thermal imaging, thermographic imaging, or thermal video, is a

type of infrared imaging science. Thermographic cameras detect

radiation in the infrared range of the electromagnetic spectrum (roughly

900–14,000 nanometers or 0.9–14 µm) and produce images of that

radiation, called thermograms. Since infrared radiation is emitted by all

objects based on their temperatures, according to the black body radiation

law, thermography makes it possible to "see" one's environment with or

without visible illumination. The amount of radiation emitted by an

object increases with temperature, therefore thermography allows one to

see variations in temperature (hence the name). When viewed by

thermographic camera, warm objects stand out well against cooler

backgrounds; humans and other warm-blooded animals become easily

visible against the environment, day or night.

The use of thermal imaging has increased dramatically with

governments and airports staff using the technology to detect suspected

swine flu cases during the 2009 pandemic. Other uses include,

firefighters use it to see through smoke, find persons, and localize the

base of a fire. With thermal imaging, power lines maintenance

technicians locate overheating joints and parts, a tell-tale sign of their

failure, to eliminate potential hazards. Some physiological activities,

particularly responses, in human beings and other warm-blooded animals

can also be monitored with thermo graphic imaging.

2.1.8 POSITRON EMISSION TOMOGRAPHY

Positron emission tomography (PET) is a nuclear medicine

imaging technique which produces a three-dimensional image or picture

32

of functional processes in the body. The system detects pairs of gamma

rays emitted indirectly by a positron-emitting radionuclide (tracer), which

is introduced into the body on a biologically active molecule. Images of

tracer concentration in 3-dimensional space within the body are then

reconstructed by computer analysis. In modern scanners, this

reconstruction is often accomplished with the aid of a CT X-ray scan

performed on the patient during the same session, in the same machine.

If the biologically active molecule chosen for PET is FDG, an

analogue of glucose, the concentrations of tracer imaged then give tissue

metabolic activity, in terms of regional glucose uptake. Although use of

this tracer results in the most common type of PET scan, other tracer

molecules are used in PET to image the tissue concentration of many

other types of molecules of interest.

2.1.9 PHOTO ACOUSTIC IMAGING

Photo acoustic imaging is a recently developed hybrid biomedical

imaging modality based on the photo acoustic effect. It combines the

advantages of optical absorption contrast with ultrasonic spatial

resolution for deep imaging in (optical) diffusive or quasi-diffusive

regime. Recent studies have shown that photo acoustic imaging can be

used in vivo for tumor angiogenesis monitoring, blood oxygenation

mapping, functional brain imaging, and skin melanoma detection etc.

2.1.10 ENDOSCOPIC IMAGING

Endoscopy is a medical tool at the forefront in the diagnosis and

treatment of human diseases. Endoscopes, inserted through orifices such

as the mouth, nose, anus, and urethra, play an instrumental role in the

management of diseases of the pharynx, esophagus, stomach, small

33

intestine, colon, larynx, bronchial tree, and urinary system. The

fundamental concepts of endoscopy, including the optical and

mechanical components of typical endoscopes are given. The field of

endoscopy continues to evolve with the aid of technological innovations

and development. Early endoscopes consisted of simple rigid tubes that

provided limited views of a few easily accessed organs. Recent

developments have substantially enhanced the capabilities of endoscopes.

For example, fiber optic imaging bundles have allowed for the

development of flexible instruments that may be guided through tortuous

organs to visualize deeply into the body.

Conventional endoscopy is based on the detection of diffusely

reflected white light from tissue surfaces to reveal neoplasm.

Advancements in optical imaging take full advantage of light's properties

such as its spectral content and coherence to improve contrast and

resolution. Ultrasound imaging has been combined with endoscopy to

enable visualization beyond the tissue surface. Novel imaging modalities

such as fluorescence imaging and optical coherence tomography (OCT)

provide even more informative images. Technological improvements that

are enhancing the capability of endoscopes to visualize, diagnose, and

treat human diseases are revolutionizing the practice of medical

endoscopy.

BRAIN TUMOR AND ITS STAGES

2.2.1 INTRODUCTION

The brain tumor is an abnormal growth of cells within the brain. Brain

tumors can be

1. Benign (Non-cancerous)

34

2. Malignant(Cancerous)

Benign brain tumors do not contain cancer cells. Usually, benign

tumors can be removed, and they seldom grow back. The border or edge

of a benign brain tumor can be clearly seen. Cells from benign tumors do

not invade tissues around them or spread to other parts of the body.

However, benign tumors can press on sensitive areas of the brain and

cause serious health problems. Unlike benign tumors in most other parts

of the body, benign brain tumors are sometimes life threatening. Very

rarely, a benign brain tumor may become malignant.

Malignant brain tumors contain cancer cells. Malignant brain

tumors are generally more serious and often are lives threatening. They

are likely to grow rapidly and crowd or invade the surrounding healthy

brain tissue. Very rarely; cancer cells may break away from a malignant

brain tumor and spread to other parts of the brain, to the spinal cord, or

even to other parts of the body. The spread of cancer is called metastasis.

Sometimes, a malignant tumor does not extend into healthy tissue. The

tumor may be contained within a layer of tissue or the bones of the skull

or another structure in the head may confine it. This kind of tumor is

called encapsulated.

2.2.2 STAGES OF TUMOR

Different stages of tumors are given as follows:

Stage 0 - A typical cells in a normal anatomical configuration

Stage 1 - Tumor limited to the local anatomical site

Stage 2 - Involvement of ipsilateral regional lymph nodes

Stage 3 - Involvement of contra lateral lymph nodes

35

Stage 4 - Involvement of a distant site

The stage together with an assessment of the degree of

differentiation is very important for treatment planning and for

determining cancer prognosis.

2.3 CAUSES OF BRAIN TUMOR

2.3.1 RACE

Brain tumors occur more often among white people than among

people of other races.

2.3.2 AGE

Most brain tumors are detected in people who are 70 years old or

older. However, brain tumors are the second most common cancer in

children. (Leukemia is the most common childhood cancer.) Brain

tumors are more common in children younger than 8 years old than in

older children.

2.3.3 FAMILY HISTORY

People with family members who have gliomas may be more

likely to develop this disease. Being exposed to radiation or certain

chemicals at work:

Radiation - Workers in the nuclear industry have an increased risk

of developing a brain tumor.

Formaldehyde - Pathologists and embalmers who work with

formaldehyde have an increased risk of developing brain cancer.

Scientists have not found an increased risk of brain cancer among

other types of workers exposed to formaldehyde.

36

Vinyl chloride - Workers who make plastics may be exposed to

vinyl chloride. This chemical may increase the risk of brain

tumors.

Acrylonitrile - People who make textiles and plastics may be

exposed to acrylonitrile. This exposure may increase the risk of

brain cancer.

2.4 SYMPTOMS OF BRAIN TUMOR

2.4.1 HEADACHES:

This was the most common symptom, with 46% of the patients

reporting having headaches. They described the headaches in many

different ways, with no one pattern being a sure sign of brain tumor.

Many - perhaps most - people get headaches at some point in their life, so

this is not a definite sign of brain tumors.

2.4.2 SEIZURES:

This was the second most common symptom reported, with 33%

of the patients reporting a seizure before the diagnosis was made.

Seizures can also be caused by other things, like epilepsy, high fevers,

stroke, trauma, and other disorders. This is a symptom that should never

be ignored, whatever the cause. In a person who never had a seizure

before, it usually indicates something serious and you must get a brain

scan. A seizure is a sudden, involuntary change in behavior, muscle

control, consciousness, and/or sensation. Symptoms of a seizure can

range from sudden, violent shaking and total loss of consciousness to

muscle twitching or slight shaking of a limb. Staring into space, altered

vision, and difficulty in speaking are some of the other behaviors that a

37

person may exhibit while having a seizure. Approximately 10% of the

population will experience a single seizure in their lifetime.

2.4.3 NAUSEA AND VOMITING:

As with headaches, these are non-specific - which means that most

people who have nausea and vomiting do not have a brain tumor.

Twenty-two percent of the people in our survey reported that they had

nausea and /or vomiting as a symptom.

2.4.4 BEHAVIORAL AND COGNITIVE PROBLEMS:

Many reported behavioral and cognitive changes, such as:

problems with recent memory, inability to concentrate or finding the

right words, acting out - no patience or tolerance, and loss of inhibitions -

saying or doing things that are not appropriate for the situation.

2.5 TESTS AND DIAGNOSIS

2.5.1 A NEUROLOGICAL EXAM.

A neurological exam may include, among other things, checking

your vision, hearing, balance, coordination and reflexes. Difficulty in one

or more areas may provide clues about the part of your brain that could

be affected by a brain tumor.

2.5.2 IMAGING TESTS.

Magnetic resonance imaging (MRI) is commonly used to help

diagnose brain tumors. MRI uses magnetic fields and radio waves to

generate images of the brain. In some cases a dye may be injected

through a vein in your arm before your MRI. A number of specialized

MRI scans may help your doctor in evaluation and treatment planning,

38

including functional MRI, perfusion MRI and magnetic resonance

spectroscopy.

2.5.3 BIOPSY.

A biopsy can be performed as part of an operation to remove the

brain tumor, or a biopsy can be performed using a needle. A stereo tactic

needle biopsy may be done for brain tumors in hard to reach areas or very

sensitive areas within your brain that might be damaged by a more

extensive operation. A neurosurgeon drills a small hole, called a burr

hole, into the skull. A narrow, thin needle is then inserted through the

hole. Tissue is removed using the needle, which is frequently guided by

computerized tomography (CT) or MRI scanning. The biopsy sample is

then viewed under a microscope to determine if it is cancerous or benign.

This information is helpful in guiding treatment.

2.6 TYPES OF TUMOR

2.6.1 Acoustic Neuroma

An acoustic neuroma is also known as a vestibular schwannoma or

neurilemmoma.

Characteristics

Grows on the sheath surrounding the eighth cranial nerve in the

inner ear.

More common in women than men.

 Symptoms

Hearing loss in one ear

Dizziness or vertigo

39

Tinnitus (ringing in the ear)

Tingling or numbness in the face

Walking and balance problems

Lack of coordination

2.6.2 Astrocytom

2.6.2.1 Pilocytic Astrocytoma

Also called: Juvenile Pilocytic Astrocytoma (JPA).

Characteristics

Slow growing, with relatively well-defined borders

Grows in the cerebrum, optic nerve pathways, brain stem and

cerebellum

Occurs most often in children and teens

Accounts for two percent of all brain tumors

2.6.2.2 Low-Grade Astrocytoma

An astrocytoma is a type of glioma that develops from star-shaped

cells (astrocytes) that support nerve cells. The WHO classifies a low-

grade astrocytoma as a grade II tumor.

Characteristics

Slow growing

Rarely spreads to other parts of the CNS

Borders not well defined

Common among men and women in their 20s-50s

40

 2.6.2.3 Anaplastic Astrocytoma

An astrocytoma is a glioma that develops from star-shaped glial

cells (astrocytes) that support nerve cells.  An anaplastic astrocytoma is

classified as a grade III tumor.

Characteristics

Grows faster and more aggressively than grade II astrocytomas

Tumor cells are not uniform in appearance

Invades neighboring tissue

Common among men and women in their 30s-50s

More common in men than women

Accounts for four percent of all brain tumors

2.6.2.4 Anaplastic Astrocytoma

An astrocytoma is a glioma that develops from star-shaped glial

cells (astrocytes) that support nerve cells.  An anaplastic astrocytoma is

classified as a grade III tumor.

Characteristics

Grows faster and more aggressively than grade II astrocytomas

Tumor cells are not uniform in appearance

Invades neighboring tissue

Common among men and women in their 30s-50s

More common in men than women

Accounts for four percent of all brain tumors

41

2.6.3 Glioblastoma Multiforme (GBM)

An astrocytoma is a glioma that develops from star-shaped glial

cells (astrocytes) that support nerve cells.  A glioblastoma multiforme is

classified as a grade IV astrocytoma.  It is also referred to as a

glioblastoma or GBM.

Characteristics

Most invasive type of glial tumor

Commonly spreads to nearby tissue

Grows rapidly

May be composed of several different kinds of cells (i.e.,

astrocytes, oligodendrocytes)

May have evolved from a low-grade astrocytoma or an

oligodendroglioma

Common among men and women in their 50s-70s

More common in men than women

Accounts for 23 percent of all primary brain tumors

2.6.4 Chordoma

Characteristics

Rare and low grade

Occurs at the sacrum, near the lower tip of the spine, or at the base

of the skull

Originates from cells left over from early fetal development

42

Invades the bone and soft tissues but rarely the brain tissue

Can block the ventricles, causing hydrocephalus

Can metastasize (spread) or recur

 Symptoms

Double vision

Headaches

2.6.5 CNS Lymphoma

CNS Lymphoma is a type of cancer that develops in the lymphatic

system. The lymphatic system is a network of small organs called lymph

nodes and vessels (similar to blood vessels) that carry a clear, watery

fluid called lymph throughout the body. This fluid supplies cells called

lymphocytes that fight disease and infection. To correctly diagnose

primary CNS Lymphoma, staging must be done. Staging is the process of

using CT scanning to examine many parts of the body. Staging helps to

confirm where the cancer originated and how far it has spread.

Characteristics

Very aggressive

Usually involves multiple tumors throughout the central nervous

system (CNS)

More common in people whose immune systems are compromised

Often develops in the brain, commonly in the areas adjacent to the

ventricles

Can be primary (originating in the brain) or secondary

43

Most common among men and women in their 60s-80s, but

incidence is increasing in young adults

Twice as common in men as in women

Accounts for three percent of all brain tumors

 Symptoms

Headaches

Partial paralysis on one side of the body

Seizures

Cognitive or speech disorders

Vision problems

2.6.6 Craniopharyngioma

Characteristics

Most common in the parasellar region, an area at the base of the

brain and near the optic nerves

Also grows in the regions of the optic nerves and the

hypothalamus, near the pituitary gland

Tends to be low grade

Often accompanied by a cyst

Originates in cells left over from early fetal development

Occurs in children and men and women in their 50s and 60s

44

Symptoms

Headaches

Visual changes

Weight gain

Delayed development in children

2.6.7 Brain Stem Glioma

Characteristics

Named for its location at the base of the brain

Can range from low grade to high grade

Occurs most often in children between three and ten years of age,

but can occur in adults

 Symptoms

Headaches

Nausea

Speech or balance abnormalities

Difficulty swallowing

Weakness or numbness of the arms and/or legs

Facial weakness

Double vision

45

Symptoms can develop slowly and subtly and may go unnoticed for

months. In other cases, the symptoms may arise abruptly. A sudden onset

of symptoms tends to occur with rapidly growing, high-grade tumors.

2.6.8 Meningioma

These tumors grow from the meninges, the layers of tissue

covering the brain and spinal cord. As they grow, meningiomas compress

adjacent brain tissue. Symptoms are often related to this compression of

brain tissue, which can also affect cranial nerves and blood vessels. In

some cases, meningioma growth can also extend into the bones of the

head and face, which may produce visible changes. Most meningiomas

are considered nonmalignant or low grade tumors. However, unlike

nonmalignant tumors elsewhere in the body, some of these brain tumors

can cause disability and may sometimes be life threatening. In many

cases, meningiomas grow slowly. Other meningiomas grow more rapidly

or have sudden growth spurts. There is no way to predict the rate of

growth of a meningioma or to know for certain how long a specific tumor

was growing before diagnosis. Meningiomas are graded from low to

high. The lower the grade, the lower the risk of recurrence and aggressive

growth.

The WHO classification divides meningiomas into three grades:

Grade I: Benign Meningioma

Grade II: Atypical Meningioma

Grade III: Malignant (Anaplastic) Meningioma

46

Characteristics

May arise after previous treatment from ionizing radiation or

excessive x-ray exposure

Common among women and men in their 40s-50s, but can occur at

any age

Twice as common in women as in men

Accounts for over 30 percent of all primary brain tumors

In very rare cases, can invade the skull or metastasize to the skin or

lungs

Women with meningiomas can experience tumor growth during

pregnancy

In rare cases, multiple meningiomas can develop at the same time

in different parts of the brain and/or spinal cord

 Symptoms

Seizures

Headaches

Nausea and vomiting

Vision changes

Behavioral and cognitive changes

Sometimes no symptoms occur and tumor is detected incidentally

47

2.6.9 Schwannoma

Also known as vestibular schwannoma and acoustic neuroma (see

acoustic neuroma).

 Characteristics

Arises from cells that form a protective sheath around nerve fibers

Typically grows around the eighth cranial nerve, but can be found

around other cranial or spinal nerves

 Symptoms

Reduced hearing in the ear on the side of the tumor when eighth

cranial nerve is involved Tinnitus (ringing in the ear)

Balance problems

Deficits depend on the nerve that is affected

2.6.10 Ependymoma

Ependymal tumors begin in the ependyma, cells that line the

passageways in the brain where cerebrospinal fluid (CSF) is produced

and stored. Ependymomas are classified as either supratentorial (in the

cerebral hemispheres) or infratentorial (in the back of the brain).

Variations of this tumor type include subependymoma, subependymal

giant-cell astrocytoma, and malignant ependymoma. Ependymoblastoma,

which occurs in infants and children under three years, is no longer

considered a subtype of ependymoma. For ependymoblastoma, see

primitive neuroectodermal tumor (PNET) in the Non-glial Tumors

section.

48

Characteristics

Usually localized to one area of the brain

Develops from cells that line the hollow cavities at the bottom of

the brain and the canal containing the spinal cord

Can be slow growing or fast growing

May be located in the ventricles (cavities in the center of the brain)

May block the ventricles, causing hydrocephalus (water on the

brain)

Sometimes extends to the spinal cord

Common in children, and among men and women in their 40s and

50s

Occurrence peaks at age five and again at age 34

Accounts for two percent of all brain tumors

 Symptoms

Severe headaches

Nausea and vomiting

Difficulty walking

Fatigue and sleepiness

Problems with coordination

Neck pain or stiffness

Visual problems

49

2.6.11 Rhabdoid Tumor

Characteristics

Rare

Highly aggressive and tends to spread throughout the CNS

Often appears in multiple sites in the body, especially the kidneys

Difficult to classify; may be confused with medulloblastoma or

PNETs

Occurs most often in young children but can also occur in adults

Symptoms

Vary depending on location of tumor in the brain or body

An orbital tumor may cause the eye to protrude

Balance problems may occur

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Chapter-3Segmentation algorithms

51

CHAPTER 3

SEGMENTATION ALGORITHMS

Segmentation refers to the process of partitioning a digital image

into multiple segments (sets of pixels) (Also known as super pixels). The

goal of segmentation is to simplify and/or change the representation of an

image into something that is more meaningful and easier to analyze.

Image segmentation is typically used to locate objects and boundaries

(lines, curves, etc.) in images. More precisely, image segmentation is the

process of assigning a label to every pixel in an image such that pixels

with the same label share certain visual characteristics.

3.1 EDGE DETECTION

An edge is the boundary between two regions with relatively

distinct gray-level properties. Edge detection is a terminology in image

processing and computer vision, particularly in the areas of feature

detection and feature extraction, to refer to algorithms which aim at

identifying points in a digital image at which the image brightness

changes sharply or more formally has discontinuities.

3.1.1 SOBEL OPERATOR

The Sobel operator is used in image processing, particularly within

edge detection algorithms. Technically, it is a discrete differentiation

operator, computing an approximation of the gradient of the image

intensity function. At each point in the image, the result of the Sobel

operator is either the corresponding gradient vector or the norm of this

vector. The Sobel operator is based on convolving the image with a

small, separable, and integer valued filter in horizontal and vertical

direction and is therefore relatively inexpensive in terms of computations.

52

On the other hand, the gradient approximation which it produces is

relatively crude, in particular for high frequency variations in the image.

The operator consists of a pair of 3×3 convolution kernels as shown in

Figure. One kernel is simply the other rotated by 90°.

These kernels are designed to respond maximally to edges running

vertically and horizontally relative to the pixel grid, one kernel for each

of the two perpendicular orientations. The kernels can be applied

separately to the input image, to produce separate measurements of the

gradient component in each orientation (call these Gx and Gy). These can

then be combined together to find the absolute magnitude of the gradient

at each point and the orientation of that gradient. The gradient magnitude

is given by equation 3.1,

(3.1)

Typically, an approximate magnitude is computed using equation 3.2,

(3.2)

which is much faster to compute.

53

The angle of orientation of the edge (relative to the pixel grid) giving rise

to the spatial gradient is given by equation 3.3,

(3.3)

Figure 3.1 Original Brain MR Image

Figure 3.2 Output of Edge Detection by Sobel Operator

54

3.1.2 CANNY OPERATOR

Canny (1986) considered the mathematical problem of deriving an

optimal smoothing filter given the criteria of detection, localization and

minimizing multiple responses to a single edge. He showed that the

optimal filter given these assumptions is a sum of four exponential terms.

He also showed that this filter can be well approximated by first-order

derivatives of Gaussians. Canny also introduced the notion of non-

maximum suppression, which means that given the presmoothing filters,

edge points are defined as points where the gradient magnitude assumes a

local maximum in the gradient direction.

Although his work was done in the early days of computer vision,

the Canny edge detector (including its variations) is still a state-of-the-art

edge detector. Unless the preconditions are particularly suitable, it is hard

to find an edge detector that performs significantly better than the Canny

edge detector.

The Canny-Deriche detector (Deriche 1987) was derived from

similar mathematical criteria as the Canny edge detector, although

starting from a discrete viewpoint and then leading to a set of recursive

filters for image smoothing instead of exponential filters or Gaussian

filters.

55

Figure 3.3 Output of Edge Detection by Canny Operator

Fig 3.3 shows the edge detection output by applying the Canny operator.

Canny operator has detected not only the tumor region also detects the

unwanted artifacts.

3.1.3 PREWITT’S OPERATOR

Prewitt is a method of edge detection in image processing which

calculates the maximum response of a set of convolution kernels to find

the local edge orientation for each pixel.Prewitt operator is similar to the

Sobel operator and is used for detecting vertical and horizontal edges in

images.

56

Various kernels can be used for this operation. The whole set of 8

kernels is produced by taking one of the kernels and rotating its

coefficients circularly. Each of the resulting kernels is sensitive to an

edge orientation ranging from 0° to 315° in steps of 45°, where 0°

corresponds to a vertical edge.

The maximum response for each pixel is the value of the

corresponding pixel in the output magnitude image. The values for the

output orientation image lie between 1 and 8, depending on which of the

8 kernels produced the maximum response.

This edge detection method is also called edge template matching,

because a set of edge templates is matched to the image, each

representing an edge in a certain orientation. The edge magnitude and

orientation of a pixel is then determined by the template that matches the

local area of the pixel the best.

The Prewitt edge detector is an appropriate way to estimate the

magnitude and orientation of an edge. Although differential gradient edge

detection needs a rather time-consuming calculation to estimate the

orientation from the magnitudes in the x- and y-directions, the Prewitt

edge detection obtains the orientation directly from the kernel with the

maximum response. The set of kernels is limited to 8 possible

orientations; however experience shows that most direct orientation

estimates are not much more accurate.

On the other hand, the set of kernels needs 8 convolutions for each

pixel, whereas the set of kernel in gradient method needs only 2, one

kernel being sensitive to edges in the vertical direction and one to the

horizontal direction. The result for the edge magnitude image is very

similar with both methods, provided the same convolving kernel is used.

57

Figure 3.4 Output of Edge Detection by Prewitt Operator

Fig 3.4 shows the edge detection output by applying the Prewitt

operator. Like the Sobel operator, Prewitt operator detects only the

boundary of object.

3.1.4 ROBERT’S CROSS OPERATOR

The Roberts Cross operator performs a simple, quick to compute,

2-D spatial gradient measurement on an image. Pixel values at each point

in the output represent the estimated absolute magnitude of the spatial

gradient of the input image at that point.

The operator consists of a pair of 2×2 convolution kernels as

shown in Figure. One kernel is simply the other rotated by 90°. This is

very similar to the Sobel operator.

58

These kernels are designed to respond maximally to edges running

at 45° to the pixel grid, one kernel for each of the two perpendicular

orientations. The kernels can be applied separately to the input image, to

produce separate measurements of the gradient component in each

orientation (call these Gx and Gy). These can then be combined together

to find the absolute magnitude of the gradient at each point and the

orientation of that gradient. The gradient magnitude is given by equation

3.4,

(3.4)

Although typically, an approximate magnitude is computed using

equation 3.5,

(3.5)

which is much faster to compute.

The angle of orientation of the edge giving rise to the spatial gradient

(relative to the pixel grid orientation) is given by:

59

Figure 3.5 Output of Edge Detection by Roberts Operator

Fig 3.5 shows the edge detection output by applying the Robert

operator. From the above outputs, all operators have failed to detect the

tumor location.

3.2 HISTOGRAM EQUALIZATION

Histogram equalization is a method in image processing of contrast

adjustment using the image's histogram. This method usually increases

the global contrast of many images, especially when the usable data of

the image is represented by close contrast values. Through this

adjustment, the intensities can be better distributed on the histogram. This

allows for areas of lower local contrast to gain a higher contrast without

affecting the global contrast. Histogram equalization accomplishes this

by effectively spreading out the most frequent intensity values.

The method is useful in images with backgrounds and foregrounds

that are both bright or both dark. In particular, the method can lead to

better views of bone structure in x-ray images, and to better detail in

60

photographs that are over or under-exposed. A key advantage of the

method is that it is a fairly straightforward technique and an invertible

operator. So in theory, if the histogram equalization function is known,

then the original histogram can be recovered. The calculation is not

computationally intensive. A disadvantage of the method is that it is

indiscriminate. It may increase the contrast of background noise, while

decreasing the usable signal.

Figure 3.6 Histogram

Figure 3.7 Output of Histogram equalized image

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The spatial domain enhancement technique, histogram equalization

improves contrast of the MR image by reassigning the brightness values

of pixels based on the image histogram. Generally, images have unique

brightness histograms. Even images of different areas of the same

sample, in which the various structures present have consistent brightness

levels wherever they occur, will have different histograms, depending on

the area fraction of each structure. Here the pixel intensities are modified

by a position invariant transformation function. The traditional histogram

equalization method for MR image suffers from the following

drawbacks:

It lacks of a mechanism to adjust the degree of enhancement.

It often causes unpleasant visual artifacts, such as over

enhancement, level saturation and raised noise level.

It could dramatically change the character of the image, e.g., the

average luminance (mean) of the image. Changing the overall

illumination of MR image will shifts the peaks in the histogram,

there is a very little scope to improve contrast by global

transformation.

3.3 THRESHOLDING TECHNIQUES

Thresholding is the simplest method of image segmentation. From

a grayscale image, thresholding can be used to create binary images.

During the thresholding process, individual pixels in an image are

marked as “object” pixels if their value is greater than some threshold

value (assuming an object to be brighter than the background) and as

“background” pixels otherwise. This convention is known as threshold

62

above. Variants include threshold below, which is opposite of threshold

above; threshold inside, where a pixel is labeled "object" if its value is

between two thresholds; and threshold outside, which is the opposite of

threshold inside (Shapiro, et al. 2001:83). Typically, an object pixel is

given a value of “1” while a background pixel is given a value of “0.”

Finally, a binary image is created by coloring each pixel white or black,

depending on a pixel's label.

Thresholding Between 100-200 Thresholding Between 175-200

Thresholding Between 200-225 Thresholding above 240

Figure 3.8 Output for various Threshold values

63

Fig 3.8 shows the output images by applying various threshold values.

The drawbacks of thresholding includes

• Threshold selection is not always straightforward.

• Pixels assigned to a single class need not form coherent regions as

the spatial locations of pixels are completely ignored.

3.4 REGION BASED SEGMENTATION

Region-based segmentation methods attempt to partition or group

regions according to common image properties. These image properties

consist of

1. Intensity values from original images, or computed values based

on an image operator

2. Textures or patterns that are unique to each type of region

3. Spectral profiles that provide multidimensional image data

These can be classified as two main classes

Merging Algorithms - in which neighboring regions are compared

and merged if they are close enough in some property.

Splitting Algorithms – in which large non-uniform regions are

broken up into small areas which may be uniform.

These algorithms which are combination of splitting and merging.

In all cases some uniformity criterion must be applied to decide if a

region should be split or two regions should be merged. This criterion is

based on some region property which will be decided by the application

64

and could be one of the measurable image attributes such as image mean

intensity, color, etc.,

Fig 3.9 Output of region based segmentation

Fig 3.9 shows the segmented image by applying the region based

algorithm. From the output tumor regions are segmented exactly but the

drawback of region based algorithm is it is difficult to identify the seed

points.

3.5 FUZZY C-MEANS ALGORITHM

Fuzzy C-Means Clustering (FCM) is also known as Fuzzy

ISODATA, for clustering technique. The aim of FCM is to find cluster

centers (centroids) that minimize a dissimilarity function. The fuzzified

c-means algorithm (Bezdek in Jang et al., 1997) allows each data point to

belong to a cluster to a degree specified by a membership grade, and thus

each point may belong to several clusters. The FCM employs fuzzy

partitioning such that a data point can belong to all groups with different

65

membership grades between 0 and 1. FCM is an iterative algorithm and it

is a method of grouping the similar types of pixels in the image.

Fuzzy c-means is different from hard c-means, mainly because it

employs fuzzy partitioning, where a point can belong to several clusters

with degrees of membership.

Clustering of numerical data forms the basis of many segmentation

and system modeling algorithms. The purpose of clustering is to identify

natural groupings of data from a large data set to produce a concise

representation of a system's behavior.

Fuzzy c-means (FCM) is a data clustering technique wherein each

data point belongs to a cluster to some degree that is specified by a

membership grade. This technique was originally introduced by Jim

Bezdek in 1981 [Bez81] as an improvement on earlier clustering

methods. It provides a method that shows how to group data points that

populate some multidimensional space into a specific number of different

clusters.

FCM starts with an initial guess for the cluster centers, which are

intended to mark the mean location of each cluster. The initial guess for

these cluster centers is most likely incorrect. Additionally, FCM assigns

every data point a membership grade for each cluster. By iteratively

updating the cluster centers and the membership grades for each data

point, FCM iteratively moves the cluster centers to the right location

within a data set. This iteration is based on minimizing an objective

function that represents the distance from any given data point to a

cluster center weighted by that data point's membership grade. By using

information returned by FCM to represent the fuzzy qualities of each

cluster.

66

A new cluster validity index is proposed that determines the

optimal partition and optimal number of clusters for fuzzy partitions

obtained from the fuzzy c-means algorithm. The proposed validity index

exploits an overlap measure and a separation measure between clusters.

The overlap measure, which indicates the degree of overlap between

fuzzy clusters, is obtained by computing an inter-cluster overlap. The

separation measure, which indicates the isolation distance between fuzzy

clusters, is obtained by computing a distance between fuzzy clusters.

A good fuzzy partition is expected to have a low degree of overlap

and a larger separation distance. Fuzzy cluster-validity criterion tends to

evaluate the quality of fuzzy c-partitions produced by fuzzy clustering

algorithms. Many functions have been proposed. Some methods use only

the properties of fuzzy membership degrees to evaluate partitions. Others

techniques combine the properties of membership degrees and the

structure of data. Major problems exist in both crisp and fuzzy clustering

algorithms. The fuzzy c-means type of algorithms use weights

determined by a power m of inverse distances that remains fixed over all

iterations and over all clusters, even though smaller clusters should have

a larger.

This method uses a different “distance” for each cluster that changes

over the early iterations to fit the clusters. Clustering refers to the process

of unsupervised partitioning of a data set based on a dissimilarity

measure, which determines the cluster shape. Considering that cluster

shapes may change from one cluster to another, it would be of the utmost

importance to extract the dissimilarity measure directly from the data by

means of a data model.

67

The fuzzy c-means (FCM) clustering algorithm has been

extensively used for pattern recognition. It has also been used in the

process of generating fuzzy rules from data. It has been used with success

in the soft segmentation of MR images and for the estimation of partial

volumes.

FCM partitions a collection of n vector Xi,i=1,2,3,……..,n. into

‘C’ fuzzy groups and finds the cluster center in each group such that a

cost function of dissimilarity measure is minimized. FCM employs fuzzy

partitioning such that a given data point can belong to several groups

with the degree of belongingness specified by membership grades

between 0 and 1.

The FCM algorithm is simply an iterative procedure. In a batch

mode operation FCM determines the cluster centers Ci and the

membership matrix U using following steps.

Step 1: Intialize the cluster centers the membership matrix U with

random values between 0 and 1 such that the following constraints are

satisfied

Step 2: Calculate ‘C’ fuzzy cluster centers Ci,i=1,2,……..,C

Step 3: Compute the cost functions

Where,

Y={yi },is the set of centers of clusters.

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Ej(xk), is a dissimilarity measure between the sample xk and the center yj

of a specific cluster j.

U=[ujk], is the c x n fuzzy c-partition matrix, containing the membership

values of all samples in all clusters.

m (1, ), is a control parameter of fuzziness.

Stop if either Jm below a certain tolerance or it is improved over

previous iteration.

Step 4: Compute a new U and repeat the steps until an optimum result is

obtained.

The performance depends on the initial cluster centers, thereby allowing

to run FCM several times, each starting with a different set of initial

cluster centers.

Figure 3.10 Output of FCM Algorithm

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Chapter-4Project description

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CHAPTER 4

PROJECT DESCRIPTION

4.1 BLOCK DIAGRAM

Fig 4.1 BLOCK DIAGRAM

4.2 WATERSHED SEGMENTATION

The watershed algorithm is an image processing segmentation

algorithm that splits an image into areas, based on the topology of the

image. The length of the gradients is interpreted as elevation information.

During the successive flooding of the grey value relief, watersheds with

adjacent catchment’s basins are constructed. This flooding process is

performed on the gradient image, i.e. the basins should emerge along the

edges. Normally this will lead to an over-segmentation of the image,

especially for noisy image material, e.g. medical CT data. Either the

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image must be pre-processed or the regions must be merged on the basis

of a similarity criterion afterwards.

A hierarchic watershed transformation converts the result into

a graph display (i.e. the neighbor relationships of the segmented regions

are determined) and applies further watershed transformations

recursively. A problem is that the watersheds will increase in width.

The marker based watershed transformation performs

flooding starting from specific marker positions which have been either

explicitly defined by the user or determined with morphological

operators. Interactive watershed transformations allow to determine

include and exclude points to construct artificial watersheds. This can

enhance the result of segmentation.

Fig 4.2 Segmentation using Watershed Algorithm

The image on the left represents the type of result obtained from the

thresholding of classical images where Watershed segmentation is

efficient. This could be a picture of coffee beans, blood cells, sand ...

Concepts of Watershed segmentation is

The concepts of watersheds and catchment basins are well known

in topography.

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Watershed lines divide individual catchment basins.

The North American Continental Divide is a textbook example of

a watershed line with catchment basins formed by the Atlantic and

Pacific Oceans.

Working the 2D function presentations, image data may be

interpreted as a topographic surface where the image gray-levels

represent altitudes.

Thus, region edges correspond to high watersheds and low-

gradient region interiors correspond to catchment basins.

The goal of region growing segmentation is to create homogeneous

regions.

In watershed segmentation, catchment basins of the topographic

surface are homogeneous in the sense that all pixels belonging to

the same catchment basin are connected with the basin's region of

minimum altitude (gray-level) by a simple path of pixels that have

monotonically decreasing altitude (gray-level) along the path.

Such catchment basins then represent the regions of the segmented

image.

One of the important drawbacks of watershed segmentation

algorithm is producing severe oversegmentation due sensitivity of noise.

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Fig 4.3 Original MRI image

Fig 4.4 Enhanced Image

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Fig 4.5 Boundary extraction of reconstructed image using watershed

algorithm

Fig 4.6 Boundary superimposed on original image

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4.3 INDEPENDENT COMPONENT ANALYSIS

4.3.1 INTRODUCTION

Independent component analysis (ICA) is a computational method

for separating a multivariate signal into additive subcomponents

supposing the mutual statistical independence of the non-Gaussian source

signals.

A simple application of ICA is the “cocktail party problem”,

where the underlying speech signals are separated from a sample data

consisting of people talking simultaneously in a room. The problem is

simplified by assuming no time delays and echoes. If N number of source

present, at least N observations are needed to get the original signals.

This constitutes the square

J = D

Where,

D = input dimension of the data

J = dimension of the model

There are two cases:

1. If (J < D) is underdetermined

2. If (J>D) is overdetermined

TYPES OF ICA

There are two types of ICA.They are

1 .Non Linear ICA

2. Linear ICA

(a) Linear Noiseless ICA

(b) Linear Noisy ICA

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4.3.1.1 LINEAR NOISELESS ICA

The components xi is a random vector are generated as a sum of

the independent components sk, weighted by the mixing weights ai,k.The

generative formula is given by

x = As

X=Mixture; A=Mixing coefficients; S=Sources.

This is called ICA MODEL. This is done by adaptively calculating

the w vectors and setting up a cost function which either maximizes the

nongaussianity of the calculated by

sk = (wT * x)

ASSUMPTIONS

1. Linear mixing

2. Independence of sources

3. Non Gaussianity

(A) LINEAR MIXING

Linear mixing based on first and second order stastics are usually

optimal. When the linear transformation takes place it leads to

gaussianty.So limited amount of information can be separated into

independent components. But when this phenomenon takes place with

higher order stastics then it does not miss out extra information which

enhances the image quality.

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(B) INDEPENDENCE OF SOURCE

If two random variables x and y are present in an image, they are

said to be independent if the information regarding x does not dependent

on y this is one of the key concept in independent component analysis.

(C) NON GAUSSIANITY

According to central limit theorem sum of non Gaussian variables

is closer to Gaussian original ones. Its non gaussianity will attain the

local maximum equal to independent components. This is because, if it

were the mixture of two are more components it would be closer to

Gaussian distribution but this is eliminated by central limit theorem. If

the contained data in an image is non Gaussian then their high order

statistics would contain extra information which makes the process

easier.

4.3.3 NEED FOR CLASSIFICATION

In magnetic resonance imaging (MRI), a set of slices are acquired

over time, and small differences in the intensity of the signal over time

are extracted. The first application of ICA to MRI data used spatial ICA

(SICA). SICA when applying ICA to MRI have several reasons:

The most important is that the spatial dimension is much larger

than the temporal dimension in MRI. By choosing a particular

component is examined through the spatial map using knowledge of

brain structure and function. A spatial ICA analysis is performed on the

data.

The application of SICA to MRI data is typically done in one of

two ways:

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Consistently task-related components are then chosen by

correlating their time courses with the predicted waveform. Transiently

task-related components are also extracted by examination of those

components that are correlated, but not as highly correlated as the

consistently task-related component.

CLASSIFICATION OF ICA

1. Spatial Independent Component Analysis (SICA)

2. Temporal Independent Component Analysis (TICA)

Fig 4.7 BLOCK DIAGRAM OF SPATIAL AND TEMPORAL ICA

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In spatial ICA, suppose X is an N-by-M matrix, where N is the

number of time points and M is the number of voxels. The “signals” are

the M spatial voxels , flattened to a 1-D vector, and there are thus N

different instances of these signals whereas TICA would consider the

signals the N individual time courses of which there are M instances.

The SICA decomposition can then be described as

C = W* X

W= N-by-N estimated linear mixing matrix

C = N-by-M matrix with N independent components.

Now,

X = Wˆ -1*C

Where the spatially independent components (images) are located in the

rows of C.

In temporal ICA, X is an M-by-N matrix. The decomposition is

C = W* X

W= M-by-M estimated linear mixing matrix

C =M-by-N matrix containing the M independent component

Now, we can write

X = Wˆ -1*C

Where the temporally independent time courses are located in the

rows of C and the associated temporally independent maps (images) are

found in the columns of Wˆ -1.

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Spatial independent components suits for MRI application because

number of voxels in MRI is independent of time space so it is

unpredictable. So SICA has good practical feasibility than the Temporal

independent component analysis.

4.3.4 PREPROCESSING STEPS IN ICA

The preprocessing method is used to segment the MR image and it

consists of two types:

1. CENTERING

2. WHITENING

4.3.4.1 CENTERING

The most basic and necessary pre-processing is to centre x, subtract its

mean vector m = E{x} where X is a zero-mean variable. This implies that

s is zero-mean as well, as can be seen by taking expectations on both

sides. This pre-processing is made solely to simplify the ICA algorithms.

4.3.4.2 WHITENING

ICA or statistical model is represented as X=AS, Where W= A-1,

this transformation takes place through the observed vector x linearly as

˜x which is white. The covariance matrix of ˜x equals the identity matrix

E{˜x˜xT } = I.

It reduces the number of parameters to be estimated by considering

the original matrix A, there are n2 parameters but we only need to

estimate the new, orthogonal mixing matrix ˜A.

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Fig 4.8 Orthogonal Mixing Matrix

JOINT DISTRIBUTION OF WHITENED MIXTURES

Because whitening is a very simple and standard procedure, it

reduces the complexity and reduces the dimension of the data. When

considering in PCA it proceeds as follows:

1. Obtain data

2. Subtract the mean

3. Calculate the covariance matrix

4. Calculate the Eigen vector and Eigen value.

Thus the highest Eigen value is obtained as principle and it also

retains the lowest Eigen value which produces noise. But in ICA Eigen

values which are too small are discarded. Thus it enhances in reducing

the noise in an image.

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4.4 COMPARISON OF PCA AND ICA

The basis images found by PCA depend only on pair wise

relationships between pixels in the image database. In a task such as

brain tumor detection, in which important information may be contained

in the high-order relationships among pixel so Independent component

analysis (ICA), a generalization of PCA, is one such method.

Applying PCA on MR Images where pixel location and brain

images are treated as observation and measures respectively which leads

to maximum variability in pixels so the input does not throw high order

statistics. So, maximum amount of data cannot be separated.

Fig 4.8 PLOT OF ICA AND PCA

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Chapter-5Result

84

CHAPTER 5

RESULT

OUTPUT OF CLASSIFIED TUMOR

Tumor Image 1 GLIOBLASTOMA

Tumor Image 2 ASTROCYTOMA

85

Tumor Image 3 LYMPHOMA

Tumor Image 4 MENINGLOMA

86

Chapter-6Conclusion

87

CHAPTER 6

CONCLUSION

The results show that Watershed Segmentation can successfully

segment a tumor provided the parameters are set properly. The watershed

method did not require an initialization while the others require an

initialization inside the tumor. The visualization and quantitative

evaluations of the segmentation results demonstrate the effectiveness of

this approach. Watershed Segmentation algorithm performance is better

for the cases where the intensity level difference between the tumor and

non tumor regions is higher. It can also segment non homogenous tumors

providing the non homogeneity is within the tumor region. This paper

proves that methods aimed at general purpose segmentation tools in

medical imaging can be used for automatic segmentation of brain tumors.

The quality of the segmentation was similar to manual

segmentation and will speed up segmentation in operative imaging.

Among the segmentation methods investigated, the watershed

segmentation is marked out best out of all others. The user interface in

the main application must be extended to allow activation of the

segmentation and to collect initialization points from a pointing device

and transfer them to the segmentation module. Finally the main program

must receive the segmented image and present the image as an opaque

volume and the type of the tumor is also detected using ICA

Algorithm.

88

appendix

89

APPENDIX

clc;

s=input('ENTER THE IMAGE FILE NAME TO

TRAIN::','s');

i=imread(s);

k=trainimage_filtering(i);

figure,imshow(k);

title('FILTERED IMAGE');

n1=imcrop(k);

n2=imcrop(k);

dd=trainimage_segment(n2);

f=ica_training(n1,n2);

disp(f);

if(f>248 || f<256)

figure,imshow('tissue1.bmp');

title('ASTROCYTOMA');

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disp('detected tumour is astrocytoma'); display disease name

elseif(f>224 || f<228)

figure,imshow('tissue2.bmp');

title(' GLIOBLASTOMA');

disp('detected tumour is glioblastoma'); display disease name

elseif(f>238 || f<240)

figure,imshow('tissue3.bmp');

title(' LYMPHOMA');

disp('detected tumour is lymphoma');

elseif(f>263 || f<290)

figure,imshow('tissue4.bmp');

title(' MENINGLOMA');

disp('detected tumour is meningioma');

else

disp('unknown detected or no tumour found');

end

disp('project completed successfully');

function [k]=trainimage_filtering(i)

d=rgb2gray(i);

91

c=input('ENTER THE CORRESPONDING VALUE FOR

FILTERING:1-SOBEL,2-PREWITT,3-MEDIAN,4-LAPLACIAN:');

switch (c)

case 1

h=fspecial('sobel');

k=imfilter(d,h);

case 2

h=fspecial('prewitt');

k=imfilter(d,h);

case 3

k= medfilt2(d,[5 5]);

otherwise

h=fspecial('laplacian');

k=imfilter(d,h);

end

Explanation for User Defined Function Segmentation:

function [n1]=trainimage_segment(g)

BW = edge(g,'canny',0.2);

[imx,imy]=size(BW);

92

msk=[0 0 0 0 0;

0 1 1 1 0;

0 1 1 1 0;

0 1 1 1 0;

0 0 0 0 0;];

B=conv2(double(BW),double(msk));

L = bwlabel(B,8);

mx=max(max(L));

[r,c] = find(L==2);

rc = [r c];

[sx sy]=size(rc);

n1=zeros(imx,imy);

for i=1:sx

x1=rc(i,1);

y1=rc(i,2);

n1(x1,y1)=255;

end

RGB = label2rgb(B);

figure,imshow(RGB,[]);

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title('SEGMENTED IMAGE(AFFECTED REGION');

end

function[hss]=ica_training(v1,v2)

M = 2;

N = 100;

v1=double(v1);

v2=double(v2);

v1=v1(1:N);

v2=v2(1:N);

v=[v1,v2];

A=ones(1,N*2);

x =v.*A;

W = eye(1,N*2);

y = x.*W;

maxiter=100;

eta=1;

/*****************************************************/

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CHAPTER 7References

95

CHAPTER 7

BIBLIOGRAPHY

1. ICGST-GVIP Journal, ISSN 1687-398X, Volume (9), Issue (III),

June’09

2. L.P. Clarke, R.P.Velthuizen, M.A. Camacho, J.J. Heine, M

Vaidyanathan, L.O. Hall, R.W. Thatcher, and M.L. Silbinger: MRI

Segmentation: Methods and Applications. Magnetic Resonance

Imaging, 1995.

3. Medical image analysis, volume 2, issue 2,march 1998.

4. Information Technology in Biomedicine, IEEE Transactions on

sep 2005.

5. Medical Image Computing and Computer-Assisted Intervention

6. MICCAI,2002. Indian Journal of Science and Technology Vol.2

No 2 (Feb. 2009) ISSN: 0974- 6846

7. Jonathan sachs (1996)”Digital Image Basics”.

8. www.icgst.com

9. www.ieeeexplorer.com

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