Computer Aided Diagnosis System For Brain Disease Analysis · 2018. 9. 20. · Computer Aided Diagnosis System For Brain Disease Analysis 112 Int J Res Med. 2016; 5(3); 110-121 e

Computer Aided Diagnosis System For Brain Disease Analysis

110 Int J Res Med. 2016; 5(3); 110-121 e ISSN:2320-2742 p ISSN: 2320-

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Ranjita Chowdhury1, Sudipta Roy

2, Samir Kumar Bandyopadhyay

3*

1Professor, Department. of Computer Science and Engineering St. Thomas‟ College of Engineering & Technology, Kolkata ,

West Bengal, 2,3Department of Computer Science & Engineering University College of Technology, University of Kolkata,

JD-2, Sector-III, Salt Lake, Kolkata 700098, India

INTRODUCTION

Medical imaging is a routine and essential

part of medicine where computerized

applications are used to assist clinicians

and radiologists to carry out daily

activities within healthcare. A number of

applications include computer aided

pathology diagnosis, computer aided

image segmentation, planning and guiding

treatment, and monitoring disease

progression based on the information

extracted from medical images. The major

advantage of this field is that health

problems can be observed directly rather

than derived from the symptoms. Health

problems can, be broken bones, brain

abnormalities, breast, and prostate cancer.

In this synopsis attention is put on

computer aided abnormal brain lesions

diagnosis from magnetic resonance

imaging (MRI). A lesion is an abnormal

lesion of the brain tissues which suppress

and occupy the normal lesions area.

Various factors that lead to abnormal brain

lesion development include brain injuries,

multiple sclerosis, hemorrhage, stroke,

vascular disorders and brain tumors. Brain

lesions are often a threat to life hence their

diagnosis and treatment is of great

importance to patients . Nowadays,

different imaging modalities are used to

acquire medical images for visualization of

internal human body structures such as

tissues of the brain and neck. The most

common imaging technologies are

computed tomography (CT) and MRI. The

advantage that MRI has over CT is that it

is harmless, since it does not use ionization

radiation, and produces high quality

images with soft tissue contrast that is

much better than that with CT1. Moreover,

MRI can distinguish tissues that have

similar intensities and are hard to

distinguish using CT scans. Proposed plan

for automated brain anomaly segmentation

are developed and applied to a large

dataset of brain PD, T1- and T2-weighted

MR images.

Problem Statement: Hand labeling of

brain pathologies in medical images is

often regarded as the gold-standard

technique to segment brain abnormalities.

This method is currently used in many

laboratories by a radiologist to monitor the

response of the brain tumors and other

abnormality before and after the treatment.

However, this approach becomes tedious

in the presence of small sized brain lesions

and time consuming due to the large

amount of data to be analyzed or the

presence of multiple tumors having

different sizes. Moreover, the results are

usually operator-dependent. So it is

necessary to develop an automated

computer aided brain pathology diagnostic

application. It can save radiologists time in

setting up a suitable treatment for a patient

diagnosed with brain tumors. Numerous

automated methods for segmenting brain

pathologies have been developed. These

methods vary depending on the

characteristics of the abnormality to be

ORIGINAL ARTICLE

ABSTRACT

BACKGROUND: Computer-aided diagnosis (CAD) systems have been the focus of several research endeavors and

it based on the idea of processing and analyzing images of different hemorrhage of the brain for a quick and accurate

diagnosis. This paper proposed for automated brain anomaly segmentation are developed and applied to a large

dataset of brain PD, T1- and T2-weighted MR images.

Key Words: Brain MRI Scans, CAD Systems, Image Processing, Image Segmentation



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segmented and the type of image modality

used2. The lesion attributes is a

challenging task for automated

segmentation since it includes the variety

of shapes and sizes the lesions may

possess. Additionally they have a

likelihood of appearing at any location and

with different intensity distributions.

Because of these factors, there is no

general brain lesions segmentation method

which can be adopted widely in every

application.

OBJECTIVES

Implement an efficient computer aided

diagnosis (CAD) technique for brain

pathologies are quite useful. The other

objective is to evaluate the accuracy of the

implemented methods against the ground

truth. The methods solve to reduce the

false detection, spurious lesion generation,

under/over segmentation problems. This

will determine how well the methods

perform under varying condition. The

remaining parts of this document are

organized as follows. Section 2 presents

the few literature survey of the different

computer aided diagnostic methods used to

detect and segment brain lesions. Section 3

represents about the contribution of our

work and finally we conclude our paper in

section 4.

Literature Review: Brief background

knowledge about the detection and

segmentation of brain abnormality is very

useful to implement computerized

prediction and analysis abnormality in MR

images with high accuracy and low error

rate. Cherifi et al.4 implemented a

classification method based on expectation

maximization segmentation. Their method

is automatic and works for both tissue

recognition and tumor extraction. Jafari et

al.5 present a neural network-based method

for automatic classification of brain MR

images. Their method classifies tissues

into three categories: normal, tumor

benign and malignant. They use the

discrete wavelet transform to obtain

features related to each MRI and applied

principal component analysis to reduce

feature dimensions to obtain more

meaningful features. After the essential

features have been extracted, a supervised

feed-forward back-propagation neural

network technique is used to classify the

subjects. Supervised brain tumor detection

methods rely on the use of manually

annotated training data. These methods

develop the model from the training data

set and use the same model to recognize

new test data at a later stage. The major

disadvantage4,5

of these methods is that

they are labor intensive and time

consuming. Also they are likely to fail to

perform well if there is an overlap of

intensity distributions between healthy and

abnormal brain tissues. Pedoia et al.6

design a fully automatic technique to

detect brain tumors using symmetry

analysis and graph-cut clustering methods.

Their approach reflects the right

hemisphere and computes voxel by voxel

differences from the left hemisphere and

the mirrored right hemisphere to derive a

volume that highlights the regions with

greater intensity difference with respect to

the background as asymmetric

components. Graph-cut is then used to

extract this area and the resulting region.

The normalized histograms of the left and

right hemisphere are computed and

histogram analysis is performed to

recognize the ill hemisphere. The

limitation of their method is that it only

recognizes hyperintense tumors, sensitivity

to noise and inhomogeneity of tumors

Due to variety of brain tumor types

and their manifestation in MR images,

most state of- the-art methods focus on

most common tumor types, i.e.

glioblastoma, or they a require specific

training database to deal with a specific

tumor type. Only few researchers, like

Islam et al.7, tried to train a developed

algorithm on one tumor type and test it on

another. However, the results were not

satisfying. In common clinical routines,

the evaluation the acquired images is

currently performed manually based on

quantitative criteria or measures such as

the largest visible diameter in axial slice.

Therefore, highly accurate methods being

able to automatically analyze scans of

brain tumor would have an enormous

potential for diagnosis and therapy

planning. However, it was shown by



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Menze et al.8 that even manual annotation

performed by expert raters showed

significant variations in areas where

intensity gradients between tumorous

structure and surrounding tissue are

smooth or obscured by bias field artefacts

or partial volume effect. Moreover, brain

tumor lesions are only defined by relative

intensity changes to healthy tissues, and

their shape, size and location are

individual for each patient, which makes

the use of common pattern recognition

algorithms impossible. Segment only the

solid section of the tumor, edema and

necrosis were not considered. Saha et al.9

located tumors in 2D MR images in axial

plane using the fast detection of

asymmetry by Bhattacharyya coefficient.

The output of the algorithm was the

bounding box around the tumor. Davuluri

et al.10

presents a rule-based hemorrhage

segmentation technique that utilizes pelvic

anatomical information to segment

hemorrhage accurately. The results show

that the proposed method is able to

segment hemorrhage very well, and the

results are promising. The results show

that the proposed method is capable of

segmenting hemorrhage well. Automated

hemorrhage segmentation, once verified

with more data, will be an important

component of computer assisted decision

making system. But, quantitative

measurement of hemorrhage such as

determining hemorrhage volume,

identifying the location of hemorrhage

does not carry out with respect to the bone,

and so forth on the basis of larger data set.

Mahmood et al. 11

presents an evaluation of

several methods using both synthetic MRI

data and real data from four healthy

subjects. The methods were evaluated in

terms of: i) tissue classification accuracy

over all tissues with respect to ground

truth, ii) the accuracy of the simulated

electromagnetic wave propagation through

the head, and iii) the accuracy of the image

reconstruction of the hemorrhage. The

segmentation accuracy was measured in

terms of the degree of overlap (Dice score)

with the ground truth. The results show

that the automatic segmentation method

hierarchical segmentation approach-

Bayesian adaptive mean shift12

has better

performance with higher segmentation

accuracy, lower signal deviation and lower

relative error compared with the other

methods. The results also indicate that

accurate segmentation of tissues leads to

accurate reconstruction of intracerebral

hemorrhage in the subject‟s brain. Prakash

et al.13

proposed a modified distance

regularized level set evolution for

hemorrhage segmentation. Method

included pre-processing as filtering and

skull removal, segmentation with modified

parameters for faster convergence and

higher accuracy and post-processing which

reduce the false positives and false

negatives. The method generates

quantitative information, which is useful

for specific decision making and reduces

the time needed for the clinicians to

localize and segment the hemorrhagic

regions. Ballin et al.14

introduce a multi-

scale approach that combines

segmentation with classification to detect

abnormal brain structures in medical

imagery, and demonstrate its utility in

automatically detecting multiple sclerosis

(MS) lesions in MRI. It produces a rich

set of features describing the segments in

terms of intensity, shape, location,

neighborhood relations, and anatomical

context. These features are then fed into a

decision forest classifier, trained with data

labeled by experts, enabling the detection

of lesions at all scales. Unlike common

approaches that use pixel-by-pixel

analysis, it utilizes regional properties that

are often important for characterizing

abnormal brain structures. Biediger et

al.15

take different approaches to the

problem of lesion segmentation and

include a number of steps, including pre-

and post-processing. It introduces a two-

step approach to improve the results of an

existing automated segmentation method.

It compares all the results to the expert

segmentations for each patient. Major

disadvantage of this method is it suffers

from spurious lesions generation. Jaina et

al.16

, propose MSmetrix, an accurate and

reliable automatic method for lesion

segmentation based on MRI, independent

of scanner or acquisition protocol and



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without requiring any training data. The

actual lesion segmentation is performed

based on prior knowledge about the

location and the appearance of lesions.

Finally, the accuracy and reproducibility

of MSmetrix compare favorably with other

publicly available MS lesion segmentation

algorithms, applied on the same data using

default parameter settings. Over

segmentation is the major problem of this

method. Over or under segmentation of

normal brain tissue and non brain part are

performed by the existing segmentation

methodology. Despite the enormous

amount of work that has been done there is

no widely accepted method to do this task.

Based on these facts, finding an automated

and accurate brain lesion detection and

segmentation method is useful and gives

researchers an opportunity to come up

with new ideas in trying to solve the

different problems.

CONTRIBUTIONS

Data set selection: For experimental

analysis images available in the

public domain are utilized that are

utilized by several research

organizations those are conducting

similar research. We have also used a

Harvard medical dataset (available:

January 2014,

http://www.med.harvard.edu/aanlib/h

ome.html) with whole brain atlas and

different type of brain diseases.

BrainWeb: Simulated Brain Database

with normal structure and ground

truth (

http://brainweb.bic.mni.mcgill.ca/brai

nweb/). We also used EASI MRI

Database for different brain

abnormality MR images

(http://www.easidemographics.com/cgi

-bin/dbmri.asp).

Pre-processing

Artefacts Removal: Many different

artefacts can occur during MRI, some

affecting the diagnostic quality, while

others may be confused with pathology.

Thus to detect any abnormalities in

brain like tumor, edema must remove

artefact otherwise it may treated as an

abnormalities in automated system or

may hampered the intelligence system.

In the first stage, threshold value is

calculated over a image to binarized a

image. A statistical method i.e. standard

deviation is used to calculate the

threshold value. In this processing

statistical descriptions separate

foreground images and background

images. Then maximum and the second

highest connected component area are

found out. The ratio of the maximum

area to that of second maximum area

are calculated if the ratio is high

(signifies that the skull are brain are

together as one component as explained

above) and if ratio is low (signifies the

skull and brain are two different

component as explained above). Then

on basis of the ratio if ratio is high then

only the component with highest area is

kept and all others are removed

otherwise if ratio is low the component

with the highest and second highest

area are kept and all others are

removed. A convex hull is calculated

for the one pixel in the image and all

regions within the convexhull are set to

one. Now the above obtained image

matrix is multiplied to the original

image matrix to obtain an image

consisting of only brain and skull and

without any artefact. Results of

proposed method have been shown in

Figure1.

Figure 1: (a) input MRI of brain image

with artefact and (b) is the

corresponding output MRI

(a) (b)

Figure 1(b) is the brain image without any

artefact. It is very helpful for the further

process like skull removal, tissue detection

and abnormality detection. Our method

does not loss any information within brain

region.

Skull Elimination: Skull removal is an

essential task for accurate detection of

brain abnormality from MRI of brain

otherwise spurious lesion may generated

Thus elimination of this a problematic

http://www.med.harvard.edu/aanlib/home.html

http://www.med.harvard.edu/aanlib/home.html



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area of brain improve the diagnosis

quality of brain by intelligence system.

Here artefact and skull elimination

processes by the automated system has

been proposed. At first binarize the

image using the statistical standard

deviation method. Then complement of

binarized image and two dimensional

wavelet decompositions is done using

„db1‟ wavelet up to level two. Re-

composition of the image is done using

the approximate coefficient of previous

step. Interpolation method is used to

resize the image of the previous step to

the original size. Then labeling of the

image is done using union find method.

After that maximum area of all the

connected components is found out this

represents the brain. All other

components except the maximum

component are removed from the image.

Convex hull is computed for these one

pixel and the entire pixels inside the

convexhull are set to 1 and outside it are

set to zero. The image of the previous

step is multiplied to original image pixel

wise and thus segmented brain is

obtained. Result of skull elimination

method has been shown below in

Figure2.

Figure 2: (b) is output MRI image

without border and (b) is the input MRI

image

(a) ( b)

MRI of brain without skull region is

shown in Figure 2 (b). It is very clear in

Figure 2(b) that only skull portion is

removed, main brain region remain same.

Magnetic Resonance of brain image

Binarization: Many segmentation needs

binarization as pre-processing as

intermediate state. MRI of brain image

binarization is very difficult due to many

pixels of brain part cannot be correctly

binarized due to extensive black

background or large variation in contrast

between background and foreground of

MRI. We have proposed a binarization

that uses mean, variance, standard

deviation and entropy to determine a

threshold value followed by a non gamut

enhancement which can overcome the

binarization problem of brain

component. The binarization technique

is extensively tested with a variety of

MRI and generates good binarization

with improved accuracy and reduced

error. Proposed method is divided into

two phases; in the first phase we enhance

our brain part and in the second part we

calculate threshold value. In first phase

foreground image contrast enhancement

techniques involve scaling and shifting

operations; the net result of these

operations on an image is that all its

pixel values above a certain reference

value, with respect to that particular

image, are pushed to a higher value

while all the pixels with level below that

point are pushed to lower gray values. In

second phase we calculate final

threshold value for the binarization using

entropy and standard deviation from the

gray MR image. Thus the proposed

binarization method is a concatenated

application of gamut less enhancement,

mean, variance, standard deviation and

entropy calculation and our new

binarization method also act as a

preprocessing of MR image of brain

image. The result of our proposed

method has been shown below in Figure

Figure 3. Input brain image (a), output

binarized image by proposed method

(b)

(a) (b)

Our method able to binarized only brain

region and it does not consider the black

back ground. In most of the binarization

technique used to binarized whole image.

But only brain region is the goal of

interest, our method binarized the human

head region and it is shown in From Figure

3(b).

Tissue detection & segmentations

Normal major tissues segmentation

Segmentation of brain tissues on MRI

images generally decides the information



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and prior knowledge of the brain. In this

section, an iterative implement of level set

methodology has been proposed for the

precise segmentation of normal and

abnormal tissues in magnetic resonance

imaging (MRI) brain images. In this

segmentation, the normal tissues such as

WM (White Matter), GM (Gray Matter)

and CSF (Cerebrospinal Fluid) with other

part of human head such as marrow, and

Muscles skin are segmented and abnormal

tissues such as hemorrhage; edema and

tumor can be segmented if any. The

segmentation done by using iterative three

region level set method based on the

concept of sharp peak. The iterative

segmented component is generating a

hierarchical structure to make correct

segmentation. We have use three phase

level set as a basic segmentation and

without using mask concept. As accuracy

is an important factor in medical imaging,

thus to improve the accuracy we using

iteration on level set as key concept.

Iteration used to divides this complex

segmentation to make segmentation easier

and accurate. Calculating the sharp peak

from histogram representation of the

images and depending on this sharp peak

we repeat our task. Once we have

performed the three region level set

segmentation with three membership

functions we clearly find out three regions.

In the proposed approach, we model the

intensity distribution in the image

partitions of level set using a Gaussian

mixture model to form a close

approximation to the actual intensity

distribution in the image. The model is

forced to inflate on smooth areas and to

stop at high-gradient locations as the speed

decreases towards zero. We have chosen

peak value three because of three region

segmentation. The block diagram of the

concept of iteration of proposed method

has been shown below in Figure4. Main

concept of segmentation is given below in

Figure 4.

Figure 4: Block diagram of Iterative

segmentation method

The procedure of peak calculation is

prepared by choosing previous three and

next three neighbor positions for each gray

value k in a circular manner into (k-3)(k-

2), (k-1), (k+1), (k+2) and (k+3)

respectively. We select sharp peak only

on pixel which is greater than previous

three and less than next three pixel

intensity. After applying above method

when segmentation method stopped, we

use maxima area MA to extract maximum

area between two regions of brain, and

maximum area always appear as left child.

Brain tissues and different fragments of

skull segmentation of normal brain are

possible by up-to level 3 decompositions

and re-compositions. In other word in level

2 all decomposed fragments have sharp

peaks less than or equals to 3. If any

abnormality presence in the brain then it

does not follow same structure shown

below and abnormality need to decompose

it greater than level 3. The process of

proposed method to accurate segmentation

has been shown below in Figure5 as

hierarchical diagram.

Figure 5. Hierarchical diagram of

tissues detection methodology for

normal brain structure. If any

abnormality is present it is detected in

level 2 and numbers of sharp peaks of a

node in level 2 will greater than three

From the above figure5 it is clear that

initially normal brain image (BI) segment

into two regions BI1 (contains WM, GM,

marrow, and muscles skin) and BI2



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(contains GM, CSF, and muscles skin).

Inputted brain image is treated as level 0

and BI1 and BI2 treated as level 1.

Number of sharp peak greater than 3 at

level 1 so we repeat the segmentation and

the segmented region of level 1 produce

the level 2. Segmented part of level 2 does

not have any sharp peak greater than 3 for

normal brain. For the entire segmented

region we place maximum area as left

child. If any segmented region in level 2

has greater than 3 number of sharp peaks

then we consider some abnormality are

present in the brain. Region BI12 and BI21

both contains GM and muscles skin, so we

add this segmented region to improve the

accuracy. Region BI11 contains WM and

marrow, and BI22 contains CSF and fats.

Finally we segment WM, marrow, GM,

muscles skin, CSF, and fats by using max

area from left to right for each parent node.

If any abnormality present we can detect it

in level 2, we also use abnormality

position detection method which is

describe in next section and rest of the

region are treated as normal segmented

tissues.

Figure 6: a) Inputted MRI, b)

segmented marrow, c) segmented white

matter, d) segmented CSF, e) segmented

gray matter, f) segmented muscles skin

(a) (b)

(c) (d)

(e) (f) The above Figure 6 is the results of

segmented regions of normal tissues. It

also removes over and under segmentation

problem of other comparable method.

Proposed method gives very good results

for segmentation of different regions from

the visualization point of reference. From

the above figure 6 it is easy to find out the

area of each segmented region. Once

quantify WM, GM, and CSF, and scan a

period of time then we can predict about

Alzheimer disease. On the other hand,

brain segmentation is a preliminary step

for the other procedures such as brain

registration, warping and pixel based

morphometry.

Corpus Callosum segmentation Corpus

Callosum (CC) is an important part of

the brain which works as major neural

pathway that connects homologous

cortical areas of the two cerebral

hemispheres. The size of CC is affected

by age, sex, neurodegenerative diseases

and various lateralized behavior in

people. Here T1 weighted MRI of brain,

usually the sagittal sections is taken

which is then followed by the automated

segmentation of the MRI slice. The

proposed method includes the following

phases: (1) Input of T1 weighted image

and refining of the image to reduce noise

(2) Segmentation of CC using area

selection and binary conversion of image

(3) Coloring of detected part in original

image. Segmentation is performed by

using binarization algorithms and

maximum area selection from sagittal

image. The work flow of our proposed

methodology can be represented by

Figure 7.

Figure 7: Workflow of the proposed

methodology

Result of CC segmentation has been

Figure 8: (a) Original image, (b)

segmented region, (c) corpus callosum

in green color

(a) (b)

(c)

Input

MRI

slice

dataset

Image

correction

and filtering

Gray-scale

and

normalization of images

Segmen

tation of CC

Detection of

mid-point and

end points for bending angle

Plot the

bending

angle of all the slices

Collect all the segmented CC to produce a 3D projection



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The segmentation method gives us the

most suitable result as per T1 weighted

MRI images are involved. In this result we

highlight the detected portion of CC and

keeping rest of the image untouched.

Results have been tested on collected

datasets each consisting sagittal plane MRI

slices with different variations of input.

Abnormality detection &

segmentation

Detection of tumor, hemorrhage and

stroke lesions

Accurate identification of correct

abnormalities is a critical step in planning

appropriate therapy. The correct

characterization of underlying pathology,

such as neoplasia, vascular malformation,

or infarction, is equally important for

conclusive diagnosis. The implemented

algorithm includes several stages such as

artefact and skull elimination constituting

preprocessing, image segmentation, and

abnormality localization. After artefact and

skull removal power law transformation

has been performed. Transform values of

γ>1 have accurately the opposite effect as

those generated with principle values of γ

<1 and to the identity transformation when

c= γ =1. Gamma correction is significant

for displaying an image appropriately on a

computer screen and particular care must

be taken to reproduce colors accurately.

By using this gamma transformation the

abnormal portion can be more prominently

projected. The total intensity, by sum of

the average and standard deviation of the

gamma transformed image is finally

selected. On the basis of the final intensity

value we find the abnormal portion which

in the form of binary output. The first

derivative denotes zero in areas of constant

gray-level values while non-zero at the

onset of a gray-level step or ramp; and

must be nonzero along ramps. As

horizontal and vertical contour detection

does not produce continuous line so, we

combined it to produce continuous line.

The results of abnormal segmentation

method have been shown below in figure

9.

Figure 9: a, f) input MRI of brain

image; b, g) abnormality visualized by

red region; c, h) contour of abnormality

region (Chronic subdural hematoma &

Cerebral hemorrhage)

a) b)

c) f)

g) h)

From figure 9 we can measure the clot

thickness, abnormal area, and localization

of lesion from MRI scan have been

successfully implemented by the proposed

system. This is very crucial for early,

reliable and accurate detection of

abnormality for providing early diagnosis

and treatment, prompt transfer of the

patient to a medical facility capable of

MRI scanning and neurological

intervention if necessary.

Multiple sclerosis lesions

identifications: Multiple sclerosis (MS)

is one of the major diseases and the

progressive MS lesion formation often

leads to cognitive decline and physical

disability. A quick and perfect method

for estimating the number and size of

MS lesions in the brain is a key

component in estimating the progress of

the disease and effectiveness of

treatments. In this section a method has

been described where adaptive

background generation and binarization

using global threshold are the key step

for MS lesions detection. Then

background image is subtracted from

binarized image to find out segmented

MS lesion. We have proposed a method

which will generate the background for

each image. Three phase level set is the

key idea to generate backgrounds for

each image. Contour detection

performed after artfact and skull removal

image. Then we add the level set image



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and the contour image to generate

adaptive background. To detect MS

lesion a global threshold selection

methodology has been done by the

combination of entropy and standard

deviation. Then subtract the binary

threshold generated image by

background image and finally get the

MS lesion. This approach better captures

the neighboring lesion properties and

produces encouraging results, with a

general improvement in the detection

rate of lesions.

Figure 10: a) input brain image, b)

segmented MS lesions, c) red portions

are affected in inputted brain image.

(a) (b)

(c)

Result of our method has been shown in

above figure 10. It is very clear that our

method find accurately identifying the

size, number of lesions and location of

lesion detections as a radiologist does.

The adaptability of the proposed method

creates a number of potential opportunities

for use in clinical practice for the detection

of MS lesions in MRI. Now look at Figure

10(b) and Figure 10(c), and you will see

three lesions of approximately the same

size and one lesion as different size. The 4

foci of inflammitory activity are clearly

not in synchrony. MS lesions may change

their position at the end of the year, this

lesion has almost disappeared, but another

has appeared just behind it, if some scan is

performed over different months. More

clear visions of different lesions with their

position have been shown below in Figure

11.

Figure 11: (a) Lesions in the top right

lobe, (b) Lesions in the near middle left

lobe, (c) Lesions in the bottom right

lobe, (d) Lesions in the bottom left lobe.

(a) (b)

(c) (d)

3-D representations from 2D slices and

Volume estimation: In the three-

dimensional (3D) construction of brain

tumour using several slices of MRI has

always been a keen interest for diagnosis

and for research purpose. In this section

we proposed an approach for 3D

construction and its volume calculation

from a series of two dimensional (2D)

MRI images. Each of the abnormality

detected MRI image are successively

pushed into a stack to construct a 3-

dimensional cube inside which it contains

the 3-dimentional constructed brain

abnormality. The volume of abnormality is

calculated from area of each MRI slices

with their inter slice distance. CAD system

tool helps the neurosurgeon to take

decision during their surgical planning. To

be able to do this, one first needs to

validate a detection and segmentation

methodology which has been done

previously.

Figure 12: Overall steps of 3D

visualization of brain abnormality

NO

YES

The flow chart for entire procedure of this

work is shown in Figure 12. This flow

After all images are inserted combine them to produce a 3D matrix

View 3D figure

STOP

Don‟t push into stack and

process next image. Push into image

stack and continue

process next

images.

START

Number of input MRI

images

Inputted images are in same dimension

Artefact removal

Detect brain abnormality

Abnormality

detected in

MRI?



2734

diagram represents the overall working

process of the proposed algorithm which

will give us a brief idea about the internal

working of the algorithm. As illustrated

from the Figure 12 we get the summarized

process of 3D construction of the brain

abnormality in the form of 3D figure

which is viewed using image processing

toolbox. The outputs of figure 13 by using

abnormality detection methodology are

shown in figure 14.

Figure 13: Inputted MRI of brain slices

to the algorithm (12 slides)

Figure 14: Detected tumour part in the

slices (as binary images)

A slice with no tumor cells is taken to be

an invalid slice and is rejected. The slices

containing tumor cells have white region

in them. These images serve as the input of

the 3D algorithm which will work on this

set of slices.

Figure 15. 3D construction output from

the binary images

In the figure 15 we see three outputs of the

3D construction algorithm. The 3D figure

generated can be rotated along any

direction and the wireframe box enclosing

the figure serves as the guidelines for the

output 3D figure while rotating it in any

direction along the axis.

Classification of Tumor: Detecting

correct type of brain tumor is a crucial task

for diagnosis and curing the tumor.

Identifying the correct type of brain tumor

can provide a fast and effective way to

plan the diagnosis of tumor. In the first

stage MRI image is taken as input and is

normalized. The second stage includes

extraction of feature vectors from the

image which results in reducing

redundancy of data and will serve as the

input to the classifier. The classifier takes

each row of feature extracted vector to

produce classified output. In this section,

we apply Adaptive Neuro Fuzzy Inference

System (ANFIS) to successfully classify

the input rows. The proposed methodology

is composed of multiple stages as

illustrated in figure 16. Initially we have

chosen tumor detection methodology from

MRI slices of brain. Then they are

normalized to an acceptable range before

being fed to feature extraction process. In

the classification step, the model is first

trained using training dataset obtained

from the image database which also

defines the class labels being used. After

the classification model is trained, it is

used to classify the testing dataset into

appropriate classes that will help us in

correct medical decision making and

diagnosis of brain tumor. After getting the

predicted output we compare them with

practical values to get the performance

measurement of the model being used. The

detailed implementation of the proposed

methodology can be given by the

following subsections.

Figure 16. Total workflow of the

proposed methodology

Each of the feature vector forms an input

rows to the classifier. These parameters

are taken to be parameters for feature

extraction they are Mean, Variance,

Skewness, Kurtosis, Entropy, Energy,

Correlation, Inertia (Contrast), Absolute

value, and Inverse Difference. Now let us

consider the following two fuzzy rules for

this model.

),(, :1 111 yxfzthenBisyandAisxIfRule

),(, :2 222 yxfzthenBisyandAisxIfRule

Using these two rules we now build an

adaptive network that can properly reflect

Input MRI Image

Normalization

Feature extraction &

creation of testing

dataset

Creation of training

dataset from image

database

Classification



2734

these rules when a mapping is done from

input to output product space.

Figure 17. ANFIS architecture

equivalent to fuzzy inference system

The adaptive network equivalent to this

fuzzy model illustrated in figure 2 where

we consider that each node in a particular

layer performs the same function. Each ith

node in the a particular layer l takes an

input from the previous layer and produces

an output ilO ,.

Figure 18. 20 input slices passed

through the normalization and feature

extraction processes.

After classification we found that slices I1-

I4 are Type 1 (Glioma); slicesI5-I8 are

Type 2 (Meningioma); slices I9-I12 are

Type 3 (Metastatic adenocarcinoma);

slices I13-I16 are Type 4 (Metastatic

bronchogenic carcinoma); slicesI17-I20

are Type 5 (Sarcoma) Automation of a

model for computing an estimate of the

type of tumor are verified by a radiologist,

and a simultaneous measure of the quality

of each phase is required to readily assess

the automated image classification and

segmentation algorithm performance. The

brain and tumor tissue identification

provides a better perceptive of the spatial

relationship; thereby lend assistance to the

adage of pre-operative treatment planning.

CONCLUSIONS

The basic objective of this research work

is to develop and integrate all the image

processing algorithms proposed on the

obtained dataset of slice images to attain

the deliverables. We plan to work with a

greater number of brain structures and

explore incorporating additional

information to guide our proposal. We

deal with two dimensional MR images in

order to detect the brain tumors and

features extraction for the applications

such as treatment and follow-up, surgery,

Individual modeling, etc. For

segmentation of the tumor we first

discussed the various type of

segmentation and detection procedure

very carefully. Analyzing the

performance of all steps gives us the

correctness of the procedure and

analyzing all steps we can say the type,

stage, dangerousness of the abnormality.

We evaluate the performance of different

section with different mathematical

metric which gives us very good results.

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