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Nov 19th, 2019

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Brain Tumor Detection Using MR Image

Processing

Submitted in partial fulfillment of the requirements for the degree of

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Bachelor of Technology

in

Electrical and Electronics Engineering

by

Ujjwal Karanwal

15BEE0260

Pranav Sethi

15BEE0081

U nder the guidance of

Prof. Sankardoss V

SELECT

VIT, Vellore.

April, 2019

2

DECLARATION

I hereby declare that the thesis entitled “ Brain Tumor Detection

using MR Image Pr ocessing “” submitted by us , for the award of the degree of Bachelor

of Technology in Electrical and Electronics Engineering to VIT is a record of

bonafide work carried out by us under the supervision of Prof.

Sankardoss V .

I further declare that the work re ported in this thesis has not been submitted

and will not be submitted, either in part or in full, for the award of any other degree or

diploma in this institute or any other institute or university.

Place: Vellore

Date: 5 th April, 2019

Signature of the Candidate

3

CERTIFICATE

This is to certify that the thesis entitled “ Brain Tumor Detection using MR

Image Processing ” submitted by Ujjwal Karanwal 15BEE0260 and Pranav Se thi

15BEE0081 , SELECT , VIT University, for the award of the degree of Bachelor of

Technology in Electrical and Electronics Engineering , is a record of bonafide work

carried out by him under my supervision during the period, 01.

12. 2018 to

30.04.2019, as p er the VIT code of academic and research ethics.

The contents of this report have not been submitted and will not be submitted

either in part or in full, for the award of any other degree or diploma in this institute or

any other institute or university. The thesis fulfills the requirements and regulations of

the University and in my opinion meets the necessary standards for submission.

Place : Vellore

Date : 5th April, 2019 Signature of the Guide

In tern al E xami n er E xtern al E xami n er

HOD: Prof. Meikandasivam S

Electrical and Electronics Engineering

4

ACKNOWLEDGEMENTS

We have taken sincere efforts in this project. However, it would not have been

possible without the kind support and help of many individuals and organizations. We

wo uld like to extend our sincere thanks to all of them.

We are highly indebted to Prof. Sankardoss V for their guidance and constant

supervision as well as for providing necessary information regarding the project & also for

their support in completing the p roject. We would like to express our gratitude towards

member s of VIT University for their kind co -operation and encouragement which help ed us

in completion of this project. Our thanks and appreciations also go to the peo ple who have

willingly helped us ou t with their abilities.

Ujjwal Karanwal

Pranav Sethi

5

Executive Summary

The given project consists of diagnosing and detection of Brain tumor using Image

Processing techniques in MATLAB. Initially, image is processed for no ise removal using

various filters . Different types of segmentation methods are used in the project to give a

comparative study of various types of segmentation methods. Various different

segmentation methods which are used in the project are as follows:

? K Means

? Fuzzy C means

? Watershed Segmentation

? Histogram Thresh holding

The segmented image then undergoes morphological operations. Finally, output image is

superimposed on the input image for better representation. The tumor is marked with pink

color in the image.

6

CONTENTS Page

No.

Acknowledgement 4

Executive Summary 5

Table of Contents 6

List of Figures 8

List of Tables 9

Abbreviations 10

Symbols and Notations 11

1 INTRODUCTION 12

1.1 Objective 12

1.2 Motivation 13

1.3 Background 14

2 PROJECT DESCRIPTION AND GOALS 15

3

TECHNICAL SPECIFICATION

16

4

DESIGN APPROACH AND DETAILS (as applicable)

17

4.1 Design Approach / Ma terials & Methods

4.2 Codes and Standards

17

20

4.3 Constraints 27

5 SCHEDULE, TASKS AND MILESTONES 28

6

PROJECT DEMONSTRATION

29

7

RESULT & DISCUSSIO N (as applicable)

38

7

8 SUMMARY 40

9

REFERENCES

41

APPENDIX A

.

8

List of Figures

Figure No. Title Page No.

4.1 Methodology 17

6.1

6.2

6.3

6.4

6.5

6.6

6.7

6.8

6.9

6.10

6.11

6.12

Input Images

Image Pre -Pro cessing

Image 1 Segmentation

Image 1 Final Stage

Input Image 2

Image 2 Pre -processing

Image 2 Segmentation

Image 2 Final Stage

Input Image 3

Image 3 Pre -processing

Image 3 Segmentation

Image 3 Final Stage

29

29

30

31

32

32

33

34

35

35

36

37

9

List of Tables

Table No. Title Page No.

7.1 Tabular Comparison of Segmentation Techniques 39

10

List of Abbreviations

MRI Magnetic Resonance Imaging

CT Computed Tomography

PET Positron Emission Tomography

K Number of clusters

FCM Fuzzy C Means

FPR FPR

FNR FNR

11

Symbols and Notations

? Sigma( Summation Symbol)

? Belongs to

Ci Number of clusters

12

1. INTRODUCTION

1.1. OBJECTIVE

The g iven undertaking comprises of diagnosing and discovery of Brain tumor utilizing

Image Processing strategies in MATLAB. At first, picture is handled for commotion

evacuation utilizing different channels. Distinctive sorts of division techniques are utilized

in the venture to give a relative investigation of different kinds of division strategies.

Different diverse division strategies which are utilized in the venture are as per the

following:

? K means

? Fuzzy C means

? Watershed Segmentation

? Histogram Thresh holdi ng

The sectioned picture at that point experiences morphological tasks. At last, yield picture is

superimposed on the information picture for better portrayal. The tumor is set apart with

pink shading in the picture.

13

1.2. MOTIVATION

Imaging tumors with more accuracy play s pivotal role in the diagnosis of tumors. It

involves high resolution techniques like MRI, CT, and PET etc. MRI is an important mean

for studying the body’s visceral structures. MRI is widely used because it gives better

quality images of the brain and ca ncerous tissues compared to the other medical imaging

techniques such as X -Ray or Computed Tomography (CT). MRI imaging is a non -invasive

technique, all the more reason to use it for imaging. The basic principle behind MRI is to

generate images from MRI sc an using strong magnetic field and radio waves of the body

which helps in investigating the anatomy of the body.

Since people are inclined to mistake, mechanizing the procedure will diminish the odds

of false location. Since the patients of tumor are expand ing and numerous individuals in

rustic regions don’t have adequate assets for discovery and treatment of the ailment, so

growing such programmed framework will be exceptionally useful. Since the accessibility

of specialists in remote zones are exceptionall y restricted, just a machine is required which

can naturally recognize the tumor and give the outcomes.

14

1.3. BACKGROUND

Cerebrum tumor creates in view of irregular cell development inside the mind. Mind

Tumor by and large grouped into two sorts Benign and Malignant tumors. Dangerous

Tumors are quickly developing destructive tissues. Kindhearted are moderate developing,

dormant malignant tumor. The vast majority of the tumors are perilous. Essential mind

tumors begin in the cerebrum. In optional kind of cerebrum tumor, the t umor ventures into

the mind from different pieces of the body.

Imaging tumors with more precision assumes crucial job in the conclusion of tumors. It

includes high goal s procedures like MRI, CT, and PET and so forth. X -ray is a vital mean

for considering the body’s instinctive structures. X -ray is generally utilized on the grounds

that it gives better quality pictures of the cerebrum and harmful tissues contrasted with t he

other therapeutic imaging strategies, for example, X -Ray or Computed Tomography (CT).

X-ray imaging is a non -intrusive strategy, even more motivation to utilize it for imaging.

The essential standard behind MRI is to produce pictures from MRI check util izing solid

attractive field and radio rushes of the body which helps in examining the life systems of the

body.

15

2. PROJECT DESCRIPTION AND GOALS

This project is bein g carried out to detect brain tumor using medical imaging

techniques. The main part of the whole process is Image Segmentation which has a very

high impact of the whole process, so four different types of Image segmentation

methods are used which are, K -me ans clustering, Fuzzy C Means, Watershed

segmentation and Histogram Thresh holding. All the four different techniques are

applied of the MR image and results are observed and verified.

A given MR Image is processed using various Image processing Technique s to bring

out or spot the tumor in the image. Different types of segmentation methods are used in

the project to give a comparative study of various types of segmentation methods.

16

3. TECHNICAL SPECIFICATIONS

The whole process is impleme nted in MATLAB. Many images were used to verify the

proper functioning of the MATLAB code. The evaluation matrices used will be as follows:

? Jaccard matrix

? Dice matrix

? FPR

? FNR

The more the value of Jaccard matrix is, the more the similarity in between the

images. The less the FPR and FNR the better the algorithm.

17

4. DESIGN, APPROACH AND DETAILS

4.1 DESIGN APPROACH / MATERIALS OR METHODS

The MR Images to be examined are col lected as data. These images are served as

input to the code which is to be implemented in MATLAB. The images are made to go

through various filters and segmentation techniques and later through morphological

operations.

Figure 4.1: Methodology

18

METHODOL OGIES AND ALGORITHMS:

The whole process is divided into two stages: First is Pre -processing of MR image and

second is a Segmentation and Morphological operation.

1. Image Pre -processing : It includes conversion of image into gray scale,

enhancement of image and noise removal. Steps in their order of execution are

discussed as follows:

? Gray Scale Conversion – Convert the image to gray scale image. Then convert

it into binary image and fill the holes using the MATLAB commands.

? Image enhancement – In this step, i mage is sharpened and contrast is adjusted

to enhance the image. Sharpening returns an enhanced version of the

grayscale image where the image features, such as edges, have been

sharpened. Increase the contrast (separation between the dark and white

colors ) of the image and saturate the high and low intensities.

? Noise Removal – In this step, three different type of filters are applied on the

image to remove the high and low intensity noise. The three filters are:

Gaussian Low pass filters, Gaussian High pass filter and Median Filter.

2. Segmentation and Morphological Operations : Four different types of

segmentation methods are performed on the images. These are as follows:

3.

? K-means Clustering – The algorithm for K -means clustering is as follows:

1. First we will choose the quantity of centroids arbitrarily for example relies

upon number of bunches

2. Presently, segment the articles inside each group.

3. It discovers segments to such an extent that pixels inside each bunch zones

close to one another as would be prudent, and as far from the items in different

groups as could be allowed.

4. The articles are in the group or not will be determined by esti mating the

separation between the bunch pixels. At the point when the determined

Euclidean separation has littlest esteem then the pixels will be bunched with the

comparing group.

5. Do the above procedure for residual bunches too. At that point, we will get

three bunches with their comparable pixels.

6. Presently, ascertain the mean of each bunch and supplant the mean qualities

with the centroid.

19

7. Rehash a similar procedure with these new centroids by giving the

quantity of emphasess until except i f the union event i.e., the mean

estimation of bunches = group centroid esteem.

? Fuzzy C Means – The algorithm for Fuzzy C Means is as follows:

Step 1: Choose the number of clusters – K

Step 2: Set initial centers of clusters c1, c2… ck.

Step 3: Classify each vector x [x , x ,….x ] T into the closest centre ci by

Euclidean distance measure ||xi -ci ||=min || xi -ci||.

Step 4: Recomputed the estimates for the cluster centers ci Let ci = [ ci1, ci2

,….cin

] T cim be computed by, cim = ?xli ? Cluster (Ixlim) /Ni Where, Ni is the

number of vectors in the i -th cluster.

Step 5: If none of the cluster centers (ci =1, 2,…, k) changes in step 4 stop;

Otherwise go to step 3.

? Watershed Segmentation – It is a standout amongst the best strategies to

assemble pixels of a picture based on their powers. Pixels falling under

comparative powers are assembled together. It is a decen t division strategy

for partitioning a picture to isolate a tumor from the picture Watershed is a

scientific morphological working apparatus. Watershed is ordinarily utilized

for checking yield as opposed to utilizing as an info division procedure since

it as a rule experiences over division and under division.

? Histogram Thresh holding – Limit division is one of the most straightforward

division techniques. The information dark scale picture is changed over into

a paired organization. The strategy depends on a limit esteem which will

change over dark scale picture into a paired picture position. The primary

rationale is the determination of a limit esteem. Some regular strategies

utilized under this division incorporate most extreme entropy technique and

k-implies grouping strategy for division.

Morphological tasks utilized are Image Erosion and Dilation. Subsequent to

changing over the picture in the parallel arrangement, some morphological tasks are

connected on the changed over twofold picture. The reason for the morphological

adminis trators is to isolate the tumor part of the picture. Presently just the tumor

segment of the picture is noticeable, appeared white shading. This bit has the most

elevated force than different districts of the picture.

20

4.2. CODES AND STANDARDS

MATLAB Code:

clc

clear all;

close all

im=imread( ‘brain5.jpg’ );

figure (1)

subplot(2,4,1);

imshow(im);

title( ‘Original Image’ );

%convert original image to gre y scale image

I2=rgb2gray(im);

[rows, columns, numberOfColorBands] = size(I2);

hold on;

subplot(2,4,2);

imshow(I2);

title( ‘Grayscale Image’ );

I3 = I2 > 40;

I3 = imfill(I3, ‘holes’ );

mask = bwconvhull(I3); %produces convex hull image

I4 = I2;

I4(~mask) = 0;

%Sharpening Image

I5 = imsharpen(I4, ‘Radius’ ,2, ‘Amount’ ,1);

hold on;

subplot(2,4,3);

imshow(I5);

title( ‘Sharpened Image’ );

%Enhancement

I6=imadjust(I5);

hold on;

subplot(2,4,4);

imshow(I6,[]);

title( ‘Enhanced Image’ );

21

PQ = paddedsize(size( I6));

% Gaussian Low -pass

d0=0.05*PQ(1);

H = lpfilter( ‘gaussian’ , PQ(1), PQ(2), d0);

F=fft2(double(I6),size(H,1),size(H,2));

lpfImage=real(ifft2(H.*F));

lpfImage=lpfImage(1:size(I6,1), 1:size(I6,2));

hold on;

subplot(2,4,5)

imshow(lpfImage,[]);

title( ‘Low -pass filter’ );

% Gaussian High -pass

d1=0.02*PQ(1);

H = hpfilter( ‘gaussian’ , PQ(1), PQ(2), d1);

F=fft2(double(I6),size(H,1),size(H,2));

hpfImage=real(ifft2(H.*F));

hpfImage=hpfImage(1:size(I6,1), 1:size(I6,2));

hold on;

subplot(2,4,6)

imsho w(hpfImage,[]);

title( ‘High -pass filter’ );

h = hpfImage;

% Median filter

I7 = medfilt2(I6, [floor(PQ(1)/100) floor(PQ(1)/100)]);

hold on;

subplot(2,4,7)

imshow(I7,[]);

title( ‘Median filter’ );

tic

%K MEANS ALGORITHM

out2=I2;

max_val=max(max(out2));

out3=out2.*(255/max_val);

im1=uint8(out3);

k=4;

22

img_hist = zeros(256,1);

hist_value = zeros(256,1);

for i=1:256

img_hist(i)=sum(sum(im1==(i -1)));

end

for i=1:256

hist_value(i)=i -1;

end

cluster = zeros(k,1);

cluster_count = zeros(k,1);

for i=1:k

cluster(i)=uint8(rand*205); % to select random centroids initially

end ;

old = zeros(k,1);

while (sum(sum(abs(old -cluster))) >k)

old = cluster;

closest_cluster = zeros(256,1);

min_distance = abs(hist_value -cluster(1));

for i=2 :k

min_distance =min(min_distance, abs(hist_value -cluster(i)));

end

for i=1:k

closest_cluster(min_distance==(abs(hist_value -cluster(i)))) = i;

end

for i=1:k

cluster_count(i) = sum(img_hist .*(closest_cluster==i));

end

for i=1:k

if (cluster_count(i) == 0)

cluster(i) = uint8(rand*255);

else

cluster(i) = uint8(sum(img_hist(closest_cluster==i).*hist_value(closest_cluster==i))/cluster_count(i));

end

end

end

23

imresult=uint8(zeros(size(im1)));

for i=1:256

imresult(im1==(i -1))=cluster(closest_cluster(i));

end ;

figure(2)

imshow(imresult,[]);

title( ‘k means output’ );

toc

tic

%Fuzzy c MEANS ALGORITHM

out2=I7;

max_val=max(max(out2));

out3=out2.*(205/max_val);

im=uint8(out3);

im=double(im);

[maxX,maxY]=size(im);

IMM=cat(3,im,im);

cc1=8;

cc2=256;

tt=0;

fuzzyfactor=1.2;

while (tt

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