neoplasm in huddling stain image in fcm by k.muthu kumar, psn college of engineering, melathediyoor

7/29/2019 Neoplasm in Huddling Stain Image in FCM by K.Muthu Kumar, PSN college of engineering, melathediyoor

1/13

International Journal of Advanced Scientific and Technical Research Issue 3 volume 2, March-April 2013

Available online on http://www.rspublication.com/ijst/index.html ISSN 2249-9954

Page 1

Effective Classification of Anaplastic Neoplasm in Huddling

Stain Image by Fuzzy Clustering Method

1B.Vijayakumar and

2Ashish Chaturvedi and

3K. Muthu Kumar

1Research Scholar, Department of Computer Science and Engineering, CMJ University, Shillong,

Meghalya, India.

2Arni School of Computer Science and Application, Arni University, Indora (Kathgarh),

Himachal Pradesh, India.3PG Scholar, Department of Computer Science and Engineering, PSN College of Engineering,Tirunelveli,Tamil

Nadu,India.

_____________________________________________________________________________________

ABSTRACT

This paper presents a new attempt for classifying and analyzing the H&E Stain of distinctive types ofbrain tumor image and perceiving the region of brain tumor in the intracranial parts of skull in the human

body alsopredicting about the meticulous tumor type based on the features remains incognito. In braintumor images classification, it is indispensable to classify the numerous assortments of tumor tissue cells

for ascertaining more efficiently.The tumor prominently headway from either Glial cells andastrocytomas, oligodendrogliomas, ependymomas and the H&E Stain of various tumor tissue image

consists of a nugget of dissimilar objects incorporates necrosis tissue, normal tissue and H&E Stain of

microscopic images through Euclidean distance metric and Fuzzy K-Means clustering craft images thatsegment the H&E image by color separation for identification of quirky tumor. The classification

procedure has been applied to nine real data sets, epitomizing different orientation in the tumor tissue

region and augmentation through spatial constraints. Experimentation is carried out o the H&E Stain ofmicroscopic malignant brain neoplasm in MATLAB Environment.

Keywords: Classification, brain tumor,MRI image, Fuzzy,Clustering,microscopic image and H&E Stainimage_____________________________________________________________________________________I.INTRODUCTION

The perlustation of a brain tumor can be very daunting for patients and their families. Even though, the

Neurosurgeons perform a biopsy or removal of brain tumor, Neuro-oncologist prescribe chemotherapy

drugs for the treatment of brain tumors,radiation oncologists prescribe the radiation therapy for brain

tumors, the Neuroradiologists who helps to review the Scan based on X-ray, CT, PET, MRI, fMRI

image of brain tumor in extracted form and the Neuropathologists can only identify the tumor of types

cells based on the information provided by Computing through some Three dimensional volumetric

projection of extended atomic Microscopic images. Hence, it is very important to trajectile the brain

tumor extracted images and classify it through the trained set of images for automation computing. Theabnormal growth of cells arises from brain and around brain structures are generally considered as a

tumor or lesion. The causes of tumors in our body are still unknown and some of the possibilities include

exposing to high penetration ionizing radiation especially continuous usage of cell phones and heredity

concerns based on family history are surmountable towards brain tumor.The tumor,also called a mass that

grows inside the intracranial region of limited space of the brain and it expansion alters the brain cells

causes some symptoms like headache,vomiting, blurred vision ( visual abnormalties ) and speech

difficulties because of the damage in the left temporal lobe or motor cortex considered as focal symptoms


2/13



Page 2

and leg cramps based on the grade of tumors.The brain tumors are classified into four grades based on the

intensity and it categories into Gliomas and non-Gliomas.The Gliomas involving in the supportive or glial

cells present in the White Matter of brain and there many types of glial tumors are

astrocytes,oligodendrocytes,Glioblastomas multiforme,brain stem gliomas, and the ependymal cells.The

Non-gliomas occur in other brain parts includes meninges (tissue coverings the brain) and nerve sheath

and the tumors arise from meninges cells are called meningiomas and tumor starts from nerve sheath are

called acoustic neuromas,vestibular schwannomas and neurilemmomas. The shape of the tumor differs

prominently and around 120 types of tumors are identified. The oligodendrocytes has fried egg shaped

cells and astrocytomas has star shaped cells [1].In this paper, we framed a new idea that combines image

processing computation towards microscopic image to identify the various types of tumors and

categorized.

Fig 1.1: Stastical data of brain tumor distribution by CBTRUS

Medical imaging helps to accurate measurement of the internal aspects of the structure of the tumor. The

Magnetic Resonance Imaging (MRI) is the most frequently used imaging technique in neuroscience and

neurosurgery for these applications. MRI creates a 3D image which perfectly visualizes anatomicstructures of the brain such as deep structures and tissues of the brain, as well as the pathologies.The

analysis of soft tissue boundaries of medical imaging involves a series of steps which includes extracting

the tumor image and analysis the microscopic tumor tissue and finally classifying the tissues based on the

Neural Network trainer for automated detection and computation of brain tumor types. The accurate

extraction of internal structures of the brain tumor is of great interest for the study and the treatment of

tumors. It aims at reducing the mortality and improving the surgical or radio therapeutic management of

tumors. In brain oncology it is also desirable to have a descriptive human brain model that can integrate

21.09

10.31

2.33.9

1.829.2

5.9

0.9

7.9

3.3

13.4

Gliblastoma

AstrocytomasEpendymomas

oligodendrogliomas

medulloblastoma

meningioma

pituitary tumor

craniopharyngioma

Nerve sheath

lymphoma

All Other


3/13



Page 3

tumor information extracted from MRI data such as its localization, its type, its shape, its anatomo-

functional positioning, as well as its influence on other brain structures. Existing methods lack significant

roles in the medical image classification characterization of abnormalities are still a challenging and

difficult task.

II. BRAIN ANATOMY

The Central Nervous System and peripheral nervous system are the two main nervous system present in

the brain structure and it consists of Gray Matter (GM) and White Matter (WM). The Gray Matter control

brain activity and cortex region cover the brain which is made of glial cells and the gray matter nuclei (colostrum ) are located deep within the white matter. The myelinated axons are considered as white

matter fibers that connect the cerebral cortex with other brain regions [3]. The cerebrospinal fluid (CSF )

consists of nutrition rich glucose,salts,enzymes and WBC's present between the lower part of brain andspinal cord.The meninges is present in the intra cranial of brain and act as protective layer.The cerebrum

parts of brain is divided into two hemisphere regions,the right and left cerebral hemisphere and consists of

four lobes including parietal,frontal,temporal and occipital lobe at the back of the brain.The cerebellum

located at the back of the brain and it consists of outer GM and internal wm.The brainstem connects to the

spinal cord consists of midbrain,pons and medulla oblongata.The diencehalon layer is the central structureof the brain and consists of thalamus,hypothalamus and pituitary gland and communicated throughventricles [4].

DENDRITE

MYELIN SHEATH

SOMA

AXON

NUCLEUS

Figure 1.2 Structure of Axon in human brain

In the vertebrate animal, the brain appears to be a most complex organ and contains many billions of

neurons depends upon the cerebral cortex,each connected through synapses or axon to another fewhundred of neurons and it communicates very quickly in irregular patterns that withstand fault tolerance.It carry trains of signal pulses and secrete hormones and it differentiate from Glial cells inside the human

body and it support metabolic support and structural support. The neurons are covered by a fattysubstance known as myelin sheath which contains rich in nerve fibers and hence the tumor cells has

tended to grow in foreign body and gain energy and similarly, Glial cells also involve in brain metabolism

through controlling the chemical fluids like ions and nutrients around the neurons and this is the mainreason tumor grows only in brain [4].


4/13



Page 4

III. BRAIN TUMOR CLASSIFICATION

A brain tumor is an intracranial mass produced by an uncontrolled growth of cells either normally foundin the brain such as neurons, lymphatic tissue, glial cells, blood vessels, pituitary and pineal gland, skull,or spread from cancers primarily located in other organs. Brain tumors are classified based on the type of

tissue involved, the location of the tumor, whether it is benign or malignant, and other considerations.Primary (true) brain tumors are the tumors that originated in the brain and are named for the cell types

from which they originated. They can be benign (non cancerous), meaning that they invade surrounding

tissues. They can also be malignant and invasive (spreading to neighboring area). Secondary or metastasisbrain tumors take their origin from tumor cells which spread to the brain from another location in thebody. Most often cancers that spread to the brain to cause secondary brain tumors originate in the breast,

and kidney or from melanomas in the skin [2].

Fig 1.3: Brain Tumor Classification by Worth Health Organization

An abnormal tissue that grows as mismanage cell division in our body system is considered as a tumor or

lesion.Brain tumors are named after the cell type from which they grow ( e.g: Gliblastoma, meningioma

or ependymomas ). They may be primary or secondary. Treatment options vary depending on the tumor


5/13



Page 5

type, size and location; whether the tumor has spread; and the age and medical health of the person.

Treatment options may be curative or focus on relieving symptoms. Of the more than 120 types of brain

tumors, many can be successfully treated. New therapies are improving the life span and quality of life for

many people [1], [8].

IV. MEDICAL IMAGE COMPUTING

The investigations to confirm the presence of a brain tumor tissue involves many modality techniques

based upon the intense usage of it. It includes CT Scan ( computed tomography ) identify the inner

aspects and structures of the inside body through x-ray.The thin region slice obtained through the rotation

of the scanner.The CT Scan localizes the tumor regions and defining its dimensions, morphology but it is

not sensitive for small pituitary tumors,brain stem tumors or low grade astrocytomas and MRI Scan

(Magnetic Resonance Imaging ) spreads rotating magnetic field inside the human body which alters the

changes inside the atomic nuclei and the information is simultaneously recorded to construct the three

dimensional image through its gradients and arbitrary orientation.It not affects the body system since it

passes non ionizing radiation [5]. The MRI specialization types range from DTI image which enables

diffusion to be measured in multiple directions to examine the connectivity of different regions in the

brain and FLAIR (Fluid Attenuated Inversion Recovery) technique used to reduce the fluid contents

inside the skull region through the pulse sequence of excited and inversion [12]. The MRI image slices

the input brain image into axial, coronal and sagittal for better enhancements are given in figure 1.4

(Courtesy to Aarthi Scans,Tirunelveli,India)

SAGITTAL VIEW AXIAL VIEW CORONAL VIEW

Fig 1.4 : MRI Scan is slicing the image in three planes

The gadolinium-based agent used to embellish the presence of soft tissues like neoplasm and blood

vessels to improve the visibility of internal structures for enhancement. It localizes the tumor and nearby

structures with a high-resolution image, it also diagnosis of sub-tentorial tumors, intra-axial tumors for

planning in three-dimensional imaging for pre-surgical analyzing. The DTI (Diffusion Tensor Imaging )

maintains the connection between the tumor border and white matter and the corresponding tumor

growth,regression are compared in the white matter while MRA (Magnetic Resonance Angiography)

establish the relationship between the tumor region and blood vessels through a contrast agent like

gadolinium to generate the signal of image in a single plane. The MRS (Magnetic Resonance

Spectroscopy) evaluates the biological information about the tumor depends on the metabolic activity and
http://en.wikipedia.org/wiki/Gadoliniumhttp://en.wikipedia.org/wiki/Gadolinium


6/13



Page 6

biochemical information.It helps to differentiate the tumor active regions from the necrosis or dead

cells.Finally, the advance method of MRI which applied in neuro-surgical planning to localize the

temporal resolution of the image by the feedback mechanism.It spreads the BOLD (Blood-oxygen-level-

dependent) agent to map the neural activity in the spinal cord and the brain[6].The PET Scan ( Positron

Emission Tomography) introduces gamma rays emitted by positron tracer and it is an optional test to

gain further details about an MRI image through the level tumor absorbs the sugar content and it

differentiate scar tissue and recurring tumor cells and send the information to a computer which creates

live image.The PET scansacquired data through multiple ring detector and it coincidence the entire tissue

layer form two or three dimensional image [7].

V.TUMOR CLUSTERING

The brain tumor is classified in various grades from pilocytic astrocytoma as Grade I to GBM as Grade

IV and it's based on the aggressive form that spreads around the CSF region in the intracranial area of the

spinal cord.

Astrocytomas Oligodendrogliomas Ependymomas

Meningiomas Medulloblastomas Gangliogliomas

Schwannomas Craniopharyngiomas Chordomas

Fig 1.5 Various types of Anaplastic tumor of H&E Stain images.


7/13



Page 7

In pathology, the tumors are differentiated by the shape and orientation of cells through the popular

staining method in histology known as Hematoxylin-andEosin stain ( H&E Stain )The hemalum is the

mixture of aluminium ions, oxidized haematoxylin and immersion of an aqueous solution of rosin which

distinguish the color shade ranges from pink, red, blue and orange. The staining through H&E dye

extracts the microscopic structures present in the sample [9]. The primary tumor starts in the brain and

metastatic tumor spread to the brain from the other part of the body. The astrocytoma in grade III as

anaplastic astrocytoma which contains mix of cells and cells grades and having tentacle like projection in

a cluster form whereas the oligodendroglioma has fried egg cells with compact nuclei in its histological

appearance.Similarly,the ependymoma morphological appearance also different and composed with

regular oval nuceli in a elongated structures[1].The below H&E Stain of tumor image are the most basic

forms of tumor and its distribution are uneven and the pathologists differentiates the various types of

microscopic tissue.The following stained images of most common brain tumor tissues orientation in

different clustering form[9].

VI. IMPLEMENTATION

The stain image consists of multiple color patterns and the luminosity and chromaticity indicates variousdistinguish regions and the Euclidean distance metric in MATLAB helps to find the difference between

two color patterns.Then, the Clustering is a way to separate groups of objects and to avoid the local

minima the K-means clustering treats each object as having a location in space.

a b c Fig 1.6: a)Chordomas tumor cells in R&E Stain, b) Image labeled by cluster index, c) Object-1 cluster

d e f d) Object-2 cluster e) Object-3 cluster f) Segment the Nuceli into Separate Image.


8/13



Page 8

It finds partitions such that objects within each cluster are as close to each other as possible, and as far

from objects in other clusters as possible. K-means clustering requires that you specify the number of

clusters to be partitioned and a distance metric to quantify how close two objects are to each other.After

discriminating the cluster into three objects, a unique label identifier acquires to individual objects for

classification.Finally, the microscopic affected tissue is separated from the unaffected tissue region and

stain mark through segmenting the H&E image by color properties and extract the brightness values of

the pixels in the cluster and threshold them using im2bw in MATLAB and the processed figure are given

below

VII. Fuzzy Cluster Image Classification

The cluster image classification of spectral information involves soft computing method to classify the

objects in the tumor tissue cluster images. The soft computing is a flexible methodology that exploits the

fault tolerance for uncertainty through neural networks and fuzzy c-Means Algorithm based on the feature

resources available in the spectrum of images. The colors of various combinations that present in the

given cluster images are Blue (B), Green (G) and red (R). In spectrum measurement, these three color

variation extract the specific spectrum information from the object through the Euclidean distance metric

and through the HIS-Model hue (H) describes the pure color in terms of the dominant wavelength and it's

given by

1

12 2

1[(R G) ( )]

2cos[(R G) ( )( )]

}{ R BHR B G B

(1)

Again, the saturation (S) gives amplification of white color debasing the real color of the image and it

given by

3*min(R,G,B)1

( )R G Bs

(2)

Then, Intensity measure is the average of all combinations of different object color by [14]

I= 1

3R G B (3)

The usefulness of three color models was studied using data from computer simulations and experimental data from

an immune-double stained tissue section..Direct use of the three intensities obtained by a color camera results in the

red-green-blue (RGB) model. By decoupling the intensity of the RGB data, the hue-saturation-intensity (HSI) modelis obtained. However, the major part of the variation in perceived intensities in transmitted light microscopy is

caused by variations in staining density. Therefore, the hue-saturation-density (HSD) transform was defined as the

RGB to HSI transform, applied to optical density values rather than intensities for the individual RGB channels. The

HSD model enabled all possible distinctions in a two-dimensional, standardized data space [15].


9/13



Page 9

Astrocytomas Oligodendrogliomas Ependymomas

Meningiomas Medulloblastomas Gangliogliomas

Schwannomas Craniopharyngioma Chordomas

Fig 1.7 Object separation (affected tumor tissue) of Anaplastic tumor of H&E Stain images.

In the next stage, the fuzzy k-Means algorithm applied for finding the intra cluster distance though

minimizing the objective function which relevant data point for a set of prototypes:

2,

1 1

1( , )

2

mN c

FCM x i x i

X i

J z vd

(1)

Here, ,x i (x=1, 2,., N, i=1, 2, c) is membership value, it denotes fuzzy membership of data point x

belonging to class I, Vi(i=1, 2,, c) is centroid of each cluster and Zx(x=1,2,.,N) is data set (pixel values


10/13



Page 10

in image), m is fuzzification parameter d2(Zx, VI) is Euclidean distance between Zx and Vi, N is the number

of data points, C is the number of clusters.

Fuzzy partition is carried on an iterative optimization of the equation (1) based on [12]:

1) Choose primary centroids Vi (prototypes).

2) Computes the degree of membership of all data set in all the clusters:

(1 / m 1)2

,

(1 / m 1)2

1

1

( , )

1

( , )

)

( )

(x i

x ic

x ii

d z v

d z v

(2)

3) Compute New centroids V1i:

,

1

,

1

N

mx

x i

t x

i N

m

x i

x

z

V

(3)

and update the degree of membership ,x i according to the equation.

4) If ,x i


11/13



Page 11

REFERENCE

[1] Stupp, Roger, et al. "European Organization for Research and Treatment of Cancer Brain Tumor and

Radiotherapy Groups; National Cancer Institute of Canada Clinical Trials Group. Radiotherapy plus

concomitant and adjuvant temozolomide for glioblastoma."N Engl J Med352.10 (2005): 987-996.

[2]Dou, W., Ruan, S., Chen, Y., Bloyet, D., and Constans, J. M. (2007). A framework of fuzzy

information fusion for segmentation of brain tumor tissues on MR images. Image and Vision Computing,

25:164171.

[3] Waxman, S. G. (1999). Correlative Neuroanatomy. McGraw-Hill, 24th edition.

[4] Jones, Edward G., and Lorne M. Mendell. "Assessing the decade of the brain."Science 284.5415

(1999): 739-739.

[5] Krupinski, Elizabeth. "The Handbook of Medical Image Perception and Techniques." Cambridge

2010

[6]Luechinger, Roger, et al. "Safety considerations for magnetic resonance imaging of pacemaker and

ICD patients."Herzschrittmachertherapie und Elektrophysiologie 15.1 (2004): 73-81.

[7] Valk, Peter E., et al., eds.Positron emission tomography: clinical practice. Springer, 2006.

[8] Surawicz, Tanya S., et al. "Brain tumor survival: results from the National Cancer Data

Base."Journal of neuro-oncology 40.2 (1998): 151-160.

[9] Wolf, Helmut K., et al. "Ganglioglioma: a detailed histopathological and immunohistochemicalanalysis of 61 cases."Acta neuropathologica 88.2 (1994): 166-173.

[10] McMaster, Jacqueline, Thomas Ng, and Mark Dexter. "Intraventricular rhabdoidmeningioma."Journal of clinical neuroscience 14.7 (2007): 672-675.

[11] Khan, Gulfaraz. "Epstein-Barr virus, cytokines, and inflammation: a cocktail for the pathogenesis of

Hodgkin's lymphoma?."Experimental hematology 34.4 (2006): 399-406.

[12]Kaghed, Nabeel Hashem, and Samaher Hussein Ali. "Generating Rules from Trained Neural Network

using FCM for Satellite Images Classification."

[13] Kaya, Metin. "Image Clustering and Compression Using An Annealed Fuzzy Hopfield Neural

Network."International Journal of Signal Processing1.2 (2005).

[14] Weeks, Arthur R., and G. Eric Hague. "Color segmentation in the HSI color space using the K-means algorithm."Electronic Imaging'97. International Society for Optics and Photonics, 1997.

[15] van der Laak, Jeroen AWM, et al. "Huesaturationdensity (HSD) model for stain recognition indigital images from transmitted light microscopy." Cytometry 39.4 (2000): 275-284.


12/13



Page 12

ACKNOWLEDGMENT

The authors are thankful to Dr. P. Suyambu, Chairman, PSN Group of

Institutions for his constant support,encouragement and his valuablesuggestion and motivation through financially for implementing theresources of the paper publications.His versatile knowledge andenthusiasmtowards multidisciplinaryresearch areas paved the way of a new foundation

to reach a milestone for younger generations research scholars to learn he is

the epitome of success.

AUTHORS

Ashish Chaturvedi is a Professor and Associate director of the Arni School of

Computer Science & Application at Arni University, Kathgarh ( Indora ), Himachal

Pradesh, India from August 2011 to till date and he worked in various posts like

Acting Director in Kishan Institute of Engineering & Technology, UP, India. He

worked as Head of the Department in the Department of Applied Sciences in Gyan

Bharti Institute of Technology from July 2008 to Feb 2011 and he has 14 years of

teaching experience in various educational Institutions. He completed his M. Tech in

Information Technology, India and other M. Tech by Research in 2012. He finished

his Ph.D ( Computer Science ) from Dr. B. R. Ambedkar University, UP, India in

2006.He is a reviewer of International Journal of Software Engineering and

Knowledge Engineering run by World Scientific Publishing Co., USA; IEEE Transaction of Fuzzy Systems;International Journal of Physical Sciences; International Journal of Computer Applications, USA. He is a patron

for series of International Scientific and Engineering Serial Journals includes International Journal of Interactive

Computer Communication (IJICC);International Journal of Emerging Science & Emerging Technology( IJESET

); International Journal of Pure & Applied Sciences ( IJPAS ) and International Journal of Arts Commerce

Management ( IJACM ). He is authoring two famous Books entitled Physics for Contemporary Engineers &

Components of Software Engineering with Galgotia Publications, New Delhi. Under his supervi sion for Ph.D in

Computer Science, there are 14 students awarded Ph.D Degree in Computer Science & Engineering on the research

related to Neural Network, Pattern Recognition and Medical Image processing. He selected and participated 6 times

in the prestigious short term courses conducted by Indian Institute of Technology Rookee, India. He published his

research paper in more than 70 International Journals including IEEE Publications. He presented and published

many research papers in International Conference and he attended more than 37 International Conference and many

National and International Seminars. His research articles entitled Consciousness in Quantum Brain published in

STANCE 2005. He gauges his research interest in applying artificial neural networks in various fields like Quantum

Physics, Medical Science, Computer Science and Geo-Science. Currently, he is exploring the fields like Neural

Networks, Fuzzy Logic, Genetic Algorithm, Pattern Recognition, Robotics, Image Processing, Quantum Physics,

Geo-Science, Medical Physics, and Graphical Authentication.


13/13



Page 13

Vijayakumar is a reseach scholar and he obtained his B.E in Computer Science and

Engineering, Sivanthi Aditanar College of Engineering and M.Tech in Computerand Information Technology in MS university in 2004 and he worked as professor

and Head of the Department of Computer Science and Engineering in various

engineering institutions for past 8 years.He is a member of Many Medical Imaging

Journals including BMC journals, SAGE Journals and Journa of the National Cancer

Institutue.He participated and proceeds in more than 20 national and international

conference and he published more than 15 international journals with impact factor

journals ( based on Thomson Reuters ).He currently pursuing his research in Bio-

Medical image computing and processing,Artifical intelligence, Neural Network and

fuzzy logic.

Muthu Kumar was born in Tirunelveli, India in 1989. He completed his Bachelor of

Information and Technology. He is currently doing M. Tech in Department of

Information Technology in PSN College of Engineering. He is a member of BMC

Journals, ACI Medical journals,SAGE journals and Journal of the National Cancer

Institute. He participated in many international conferences in various states and he

published various International Journals related to brain tumor image Computing. His

research interests primarily focus on image processing, especially in the methods

related to Biomedical image computing and processing, Robotic Surgery and

Hologram .

neoplasm in huddling stain image in fcm by k.muthu kumar, psn college of engineering, melathediyoor

Documents