The document is a dissertation submitted by Deepika Joshi towards the partial fulfillment of requirements for the degree of Master of Technology in Computer Engineering from Poornima University in Jaipur, India. It discusses the development of an efficient technique for brain tumor extraction from MRI images using entropy measures. The technique aims to effectively detect and diagnose tumors in early stages. Various entropy measures such as Shannon, Renyi, Havrda-Charvat, Kapur and Vajda entropy are used to select thresholds and segment tumors from MRI images. Simulation results for different entropy measures are presented and analyzed.

1. An efficient Brain Tumor Extraction from MRI Images using Entropy Measures 2014-15
Poornima University, Jaipur M. Tech. (Computer Engineering) Page ii
CERTIFICATE
This is to certify that Deepika Joshi, Registration No. 2013PUSETMCEX02345, student of M.
Tech. in Computer Engineering branch, Department of Computer Engineering, School of
Engineering & Technology has submitted this dissertation entitled “An efficient Brain Tumor
Extraction from MRI Images using Entropy Measures” under the supervision of Mr.
Devendra Kumar Somwanshi, Assistant Professor, Department of Computer Engineering,
Poornima University towards partial fulfillment of the requirements for the Degree of M. Tech.
from the Poornima University.
Dr. Mahesh Bundele Dr. Manoj Gupta
Dean, Research & Development Dean, SET

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Poornima University, Jaipur M. Tech. (Computer Engineering) Page iii
CANDIDATE’S DECLARATION
I hereby declare that the work which is being presented in this dissertation entitled, “An efficient
Brain Tumor Extraction from MRI Images using Entropy Measures” in the partial
fulfillment for the award of the Degree of Master of Technology in Computer Engineering
branch, Department of Computer Engineering, School of Engineering & Technology, Poornima
University, Jaipur, is an authentic record of original work done by me during the period from
January, 2015 to July, 2015 under the supervision and guidance of Mr. Devendra Kumar
Somwanshi, Assistant Professor, Department of Computer Engineering, Poornima University.
I have not submitted the matter embodied in this dissertation for the award of any other
degree.
Dated: Deepika Joshi
Place: Jaipur 2013PUSETMCSX02345
SUPERVISOR’S CERTIFICATE
This is to certify that this dissertation is based on original work done by the candidate under my
supervision and to the best of my knowledge; this work has not been submitted elsewhere for the
award of any degree.
Dated: Mr. Devendra Kumar Somwanshi
Place: Jaipur Assistant Professor
Department of Computer Engineering

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Poornima University, Jaipur M. Tech. (Computer Engineering) Page iv
ACKNOWLEDGEMENT
I would like to express my deep gratitude and thanks to my Guide Mr. Devendra
Kumar Somwanshi, Assistant Professor, Department of Computer Engineering, Poornima
University for giving me an opportunity to work under his guidance for my Dissertation Work. I
would also express my sincere thanks to Dr. Mahesh Bundele, Dean, Research and
Development, Poornima University, for his consistent motivation & direction in this regard for
his support in Dissertation Work. I extend my deep sense of gratitude and respect towards
honorable Dr. S. M. Seth, Chairperson, Poornima University and Chairman, Poornima
Foundation for his continuous inspiration and motivation for the research.
I would like to express my deep gratitude to Dr. K. K. S. Bhatia, President, Poornima
University for his kind support and guidance from time to time. My sincere thanks are due to
Mr. Shashikant Singhi, Secretary, Shanti Education Society & Director General, Poornima
Foundation, who has established Poornima University and given us an opportunity to undergo
research work in this university.
I extend my sincere thanks to Dr. Manoj Gupta, Provost and Dean (SET and SBA),
Poornima University for his continuous support and encouragements throughout the course work
of my Master program.
I would like to express my sincere thanks to Dr. Chandni Kirplani, Registrar, Poornima
University for her support.
Deepika Joshi

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Poornima University, Jaipur M. Tech. (Computer Engineering) Page v
ABSTRACT
Magnetic Resonance Imaging (MRI) is increasingly being used in medical field because of its
ability to produce, non-invasively, high quality images of the inside of the human body. Since its
introduction in early 70’s, more and more complex acquisition techniques have been proposed,
raising MRI to be exploited in a wide spectrum of applications. Medical imaging seeks to reveal
internal structures hidden by the skin and bones, as well as to diagnose and treat disease. In that
way, MRI has become a useful medical diagnostic tool for the diagnosis of brain and other
medical images. Brain Tumor extraction and its analysis are challenging tasks in medical image
processing because brain image is complicated. Detection of brain tumor is one of the emerging
topics of research in biomedical image processing. Accurate detection is critical, especially when
the tumor morphological changes remain subtle, irregular and difficult to assess by clinical
examination. Brain Tumor is one of the frequent and leading causes of mortality, especially in
developed countries. Though brain tumor leads to death, early detection can increase the survival
rate.
In this dissertation work the main emphasis laid on to design an approach, which is a detection
technique so that the proposed effectively detects and diagnose the tumor in their early stage. In
this the threshold selection is done on the basis of different entropy measures such as Shannon,
Renyi, Havrda-Charvat, Kapur and Vajda entropy measures that has been used in order to detect
the Brain Tumor from MRI Images. Simulation results for different entropy measures are also
presented. At the end of the process tumor is extracted from the MRI images and its exact
position and shape are determined and various parameters like contrast, angular momentum and
entropy value have been calculated.
Here an efficient detection of brain tumor has been introduced and it has been observed that
Havrda Charvat Entropy measure provides satisfactory results in early detection of Brain Tumor.
The proposed work has been applied on MRI Images in order to get more clear and enhanced
picture of the Tumor for its early detection. Thus the developed approach is introduced to solve
the problem for tumor case of clinical MRI analysis and extracted the size and other dimensions
of tumor automatically by accurately computing the abnormal tissue areas.

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Poornima University, Jaipur M. Tech. (Computer Engineering) Page vi
TABLE OF CONTENTS
Cover Page i
Certificate ii
Candidate’s Declaration iii
Supervisor’s Certificate iii
Acknowledgement iv
Abstract v
Table of contents vi-ix
List of tables x…
List of Figures xi-xii
List of Acronyms xiii
Chapter 1 Introduction 1-10
1.1 Brain Anatomy Overview 1
1.1.1 Brainstem 2
1.1.2 Cerebellum 2
1.1.3 Frontal Lobe 2
1.1.4 Occipital Lobe 2
1.1.5 Parietal Lobe 2
1.1.6 Temporal Lobe 3
1.2 Brain tumors 3
1.3 MRI Images 4
1.4 The Image Segmentation 6
1.5 Application of Image Segmentation 6
1.5.1 Content Based Image Retrieval 6
1.5.2 Machine Vision 7
1.5.3 Medical Imaging 7
1.5.4 Object Detection 7
1.6 Introduction to Brain Tumor Segmentation 8
1.7 Difficulties in segmentation of Brain MRI 8
1.8 Thresholding 8

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Poornima University, Jaipur M. Tech. (Computer Engineering) Page vii
1.10 Thresholding Algorithm 9
1.10.1 Global thresholding algorithms 9
1.10.2 Local or adaptive thresholding algorithms 9
1.11 Thresholding Methods 9
1.11.1 Histogram Shape-Based Methods 9
1.11.2 Clustering Based Methods 9
1.11.3 Entropy Based Methods 9
1.11.4 Object Attribute-Based Methods 10
1.11.5 Spatial Methods 10
1.11.6 Local Methods 10
Chapter 2 Literature Review 11-34
2.1 Review Process Adopted 11
2.1.1 Stage 0: Get a “feel” 12
2.1.2 Stage 1: Get a “picture” 12
2.1.3 Stage 2: Get the “details” 13
2.1.4 Stage 3: “Evaluate the details” 13
2.1.5 Stage 3+: “Synthesize the detail” 13
2.2 Categorical Reviews in An efficient Brain Tumor extraction
from MRI Images using Entropy Measures 14
2.2.1 Review outcome in the issue 13
2.2.2 Common findings obtained in the issue 26
2.3 Issue wise solution approaches 27
2.4 Strengths and weaknesses 32
2.4.1 Strengths 32
2.4.2 Weaknesses 33
2.5 Gaps 34
2.6 Problem Statement 34
2.7 Objectives 34
Chapter 3 Theoretical Aspects 36-39
3.1 Classification of Image Segmentation 36
3.1.1 Thresholding Method 37

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Poornima University, Jaipur M. Tech. (Computer Engineering) Page viii
3.1.2 Edge Based Segmentation Method 37
3.1.3Region Based Segmentation Method 38
3.1.4 Clustering Based Segmentation Method 38
3.1.5 Watershed Based Segmentation Method 38
3.1.6 Partial Differential Equation Based Segmentation
Method 39
3.1.7 Artificial Neural Network Based Segmentation
Method
3.1.8 Comparison of various segmentation technique 39
3.2 Entropy Measures 39
Chapter 4 Design aspects of Proposed Work 44-55
4.1 System Design of the work 44
4.1.1 Input Image 44
4.1.2 Preprocessing 45
4.1.3 Feature Extraction 45
4.1.4 Segmentation 45
4.1.5 Entropy Calculation 46
4.1.6 Diagnosis 46
4.2 Details of data used 46
4.3 Design and Implementation of the work carried out 47
4.3.1 Algorithm 47
4.3.2 Process Flow Diagram 47
4.3.2.1 Flow chart for Shannon Entropy 49
4.3.2.2 Flow chart for Renyi Entropy 50
4.3.2.3 Flow chart for Havrda-Charvat Entropy 51
4.3.2.4 Flow chart for Kapur Entropy 52
4.3.2.5 Flow chart for Vajda Entropy 53
4.4 Details of Software 54
4.4.1 Software Specification 54
4.5 Details of Performance Parameters 54
4.5.1 Contrast 54

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Poornima University, Jaipur M. Tech. (Computer Engineering) Page ix
4.5.2 Homogeneity 54
4.5.3 Dissimilarity 55
4.5.4 Entropy 55
Chapter 5 Experimental Results and Analysis 56-74
5.1 Experimental Results and discussions 56
5.2 Result Analysis 64
5.3 Future Work 73
Chapter 6 Conclusion 75
References 77-79

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LIST OF TABLES
Table No. Table Name Page No.
2.1
Stage 2 questions along with probable location of answers in
the papers
10
2.3 Categorical Review of Research Paper 26
2.4 Comparison of different Entropy Measures 30
3.1 Comparison of various Segmentation Techniques 38
5.1 To Perform Shannon Entropy 56
5.2 To Perform Vajda Entropy 58
5.3 To perform Kapur Entropy 60
5.4 To perform Renyi Entropy 62
5.5 To perform Havrda-Charvat Entropy 64
5.6 Result analysis using different entropies 66
5.7 Result Analysis through different MRI Images 67
5.8
Texture Feature of different MRI Images for Shannon
Entropy
68
5.9 Texture Feature of different cases for Renyi Entropy 69
5.10 Texture Feature of different cases for Kapur Entropy 70
5.11 Texture Feature of different cases for Vajda Entropy 70
5.12
Texture Feature of different cases for Havrda-Charvat
Entropy
70
5.13 Data Analysis of different Entropy Function 71

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LIST OF FIGURES
Figure No. Figure Caption Page No.
1.1 Subdivision of Human Brain 2
1.3 Clinical Diagnosis of patient’s head 5
1.4 Three planes of clinical diagnosis 5
2.1 Review Process Stage Analysis 12
3.1 Image Segmentation Techniques 36
4.1 System Block Diagram 44
4.2 Process flow diagram 47

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LIST OF ACRONYMS
CI Computational Intelligence
CNN Cellular Neural Network
CT Computed Tomography
CPU Central Processing unit
EEG Electroencephalograms
GA Genetic Algorithms
GFO Generalized Fuzzy Operator
HSOM Hierarchical Self Organizing Map
HTTP Hyper Text Markup Language
IKFCM Improved Kernel Fuzzy C-Means
I/O Input /Output
MA Monitoring Agent
MRI Magnetic Resonance Imaging
OLTP Online Transaction Processing Test
PDI Provable Data Integrity
PET Positron Emission Tomography
PSO Particle Swarm Optimization
SLA Service Level Agreement
SVM Support Vector Machine
SWA Segmented Weighted Aggregation
TCCP Trusted Cloud Computing Platform
TLS Transport level security
TMR Triple Modular Redundancy
TTP Trusted Third Party