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  Siti Hasmah Digital Library AUTOMATED CLASSIFICATION AND ANNOTATION OF COMPUTED TOMOGRAPHY BRAIN IMAGES TONG HAU LEE DOCTOR OF PHILOSOPHY MULTIMEDIA UNIVERSITY SEPTEMBER 2015 PREVIEW ProQuest Number:

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  Siti Hasmah Digital Library AUTOMATED CLASSIFICATION AND ANNOTATION OF COMPUTED TOMOGRAPHY BRAIN IMAGES

  BY

TONG HAU LEE

  B.Sc. Physics (Hons), University of Science Malaysia, Malaysia M.Sc. IT, University of Science Malaysia, Malaysia

  THESIS SUBMITTED IN FULFILMENT OF THE REQUIREMENT FOR THE DEGREE OF DOCTOR OF PHILOSOPHY (by Research) in the

  Faculty of Computing and Informatics

  MULTIMEDIA UNIVERSITY MALAYSIA

  September 2015

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  Siti Hasmah Digital Library © 2015 Universiti Telekom Sdn. Bhd. ALL RIGHTS RESERVED.

  Copyright of this thesis belongs to Universiti Telekom Sdn. Bhd. as qualified by Regulation 7.2 (c) of the Multimedia University Intellectual Property and Commercialisation Policy. No part of this publication may be reproduced, stored in or introduced into a retrieval system, or transmitted in any form or by any means (electronic, mechanical, photocopying, recording, or otherwise), or for any purpose, without the express written permission of Universiti Telekom Sdn. Bhd. Due acknowledgement shall always be made of the use of any material contained in, or derived from, this thesis.

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  Siti Hasmah Digital Library DECLARATION

  I hereby declare that the work has been done by myself and no portion of the work contained in this thesis has been submitted in support of any application for any other degree or qualification on this or any other university or institution of learning.

  _______________

  Tong Hau Lee PREVIEW

  Siti Hasmah Digital Library ACKNOWLEDGEMENT

  I would like to express my utmost gratitude to the following people who have contributed and supported me throughout the completion of my studies. To my supervisor, Associate Prof. Dr. Mohammad Faizal Ahmad Fauzi and co-supervisor, Associate Prof. Dr. Haw Su Cheng, thank you for your patience in bearing with me as well as for the countless technical and life advice that you have given me. To my loving wife Tan Chai Hoon, thank you for encouraging and challenging me to stay the course and not give up in my studies. To my colleagues and friends, Dr. Ng Hu, Mr. Timothy Yap, Dr. Joshua Yap, and Mr. Looi Eng Seng; thank you for showing me that it can be done, and for lifting my spirits when things weren’t going so well. I would like to thank Dr. Fatimah Othman from Putrajaya Hospital, Dr. Ezamin Abdul Rahim and Dr. Noraini Abdul Rahim from Serdang Hospital for the image acquisition, annotation and consultation. Last but not least, thanks be to God who provides for all things and makes all things work for the good of those whom He called.

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  Siti Hasmah Digital Library DEDICATION This thesis is dedicated to my family members.

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  ABSTRACT Siti Hasmah Digital Library

  Brain hemorrhage detection is clinically crucial for the patients having head trauma and neurological disturbances. Early finding and accurate diagnosis of the brain abnormalities is one of the key contributions for the execution of the successful therapy and proper treatment. Multi-slice Computed Tomograph (CT) scans are widely employed in today’s examination of head traumas due to its effectiveness to disclose some abnormalities such as brain hemorrhages and so on. However, radiologists have to manually analyse the CT slices for the presence of brain hemorrhages. Due to the large volume of CT scan examinations, it is important to develop a computerised system that can assist the radiologists to automatically detect the presence of the brain abnormalities as well as automatically retrieve the images.

  This thesis presents an automated annotation and classification of the CT brain images. The main objective is to propose a new methodology to annotate and classify the different types of brain hemorrhages which are intra-axial, subdural and extradural hemorrhages. Besides, this thesis also aims to evaluate and investigate the effectiveness and suitability of different segmentation and classification techniques as well as introduce the new features for the classification.

  Three separate annotation processes are proposed which are the annotation of intracranial hemorrhagic slices, annotation of intra-axial hemorrhages, and annotation of subdural and extradural hemorrhages. The annotation of hemorrhagic

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  slices is a two-class classification problem to differentiate the non-hemorrhagic slices from the hemorrhagic slices. The annotation of the intra-axial slices is also a two- class classification problem to distinguish the intra-axial and non-intra-axial slices. Lastly, the annotation of subdural and extradural hemorrhages is a three-class classification problem to classify the subdural, extradural and non-extra-axial.

  The contributions of this research are many folds. The main contribution of this work is a methodology of adopting the three annotation processes rather than the employment of a single annotation process for conventional methods. Besides, in intra-axial slices annotation, a novel midline approach is proposed to better partition

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  the left and right hemispheres. On top of these, a two-level auto-enhancement is proposed to enhance the contrast of the images prior to the annotation process. For annotation of subdural and extradural, new features are also proposed. For the segmentation part, from the experimental results, K-means clustering produced the best segmentation results with the least over-segmentation problem. Lastly for contribution in classification part, the experimental results showed that Support Vector Machine (SVM) with Radial Basis Function (RBF) scored the highest precision and recall for all three annotation processes.

  Two datasets obtained from two collaborating hospitals are used to evaluate the proposed system. In total, there are 519 CT brain images used. The performance of the three separated annotation is evaluated by using different classifiers which are support vector machine, linear discriminant analysis and fuzzy k-nearest neighborhood. From the experimental results, the highest correct classification rate for the annotation of hemorrhagic slices, annotation of intra-axial and annotation of subdural and extradural are 90.8%, 88.8% and 90.6% respectively.

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  TABLE OF CONTENTS Siti Hasmah Digital Library

COPYRIGHT PAGE

  ii DECLARATION iii ACKNOWLEDGEMENT iv DEDICATION v ABSTRACT vi TABLE OF CONTENTS viii LIST OF TABLES xii LIST OF FIGURES xiv LIST OF ABBREVIATIONS xvii

CHAPTER 1: INTRODUCTION

  1

  1.1 Research Overview

  1

  1.2 Motivation of Research

  6

  1.3 Research Problems

  7

  1.4 Research Objectives

  9

  1.5 Scope of Research

  9

  1.6 Contributions to Knowledge

  12

  1.7 Research Approach Overview

  13

  1.8 Organization of the Thesis

  14 CHAPTER 2: LITERATURE REVIEW

  16

  2.1 Overview of Medical Imaging

  2.2 Overview of Adopted Medical Imaging: Computed Tomography

  17

  2.3 Overview of Brain Abnormalities

  18

  16 PREVIEW

  2.4 Type of Intracranial Hemorrhages

  21

  2.5 Intracranial Hemorrhage Detection Approach

  22

  2.6 Summary

  39 CHAPTER 3: PREPROCESSING, CLUSTERING AND DETECTION

  41

  3.1 Overview

  41

  3.2 Medical Data

  3.3 Preprocessing

  43

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  3.3.1 Original Image Enhancement

  43

  3.3.2 Parenchyma Extraction

  47

  3.3.3 Hemorrhagic Regions Contrast Enhancement

  47

  3.4 Potential Hemorrhagic Region Clustering

  49

  3.4.1 Otsu Method

  50

  3.4.2 K-means Segmentation

  51

  3.4.3 FCM Segmentation

  52

  3.4.4 Expectation-maximization Segmentation

  53

  3.5 Midline Detection

  55

  3.6 Image Enhancement Techniques Discussion and Comparison Results

  57

  3.6.1 Original Image Contrast Enhancement Results and Discussion

  58

  3.6.2.Hemorrhagic Region Contrast Enhancement Results and Discussion

  62

  3.7 Clustering Results and Discussion

  62

  3.8 Results of the Midline Approach and Discussion

  64 CHAPTER 4: ANNOTATION AND CLASSIFICATION

  66

  4.1 Overview

  66

  4.2 Annotation Process of Hemorrhagic Slice

  67

  4.3 Annotation Process of Intra-Axial

  73

  4.4 Annotation Process of Subdural and Extradural

  4.5 Feature Selection Techniques

  83

  4.5.1 Particle Swarm Optimization Search

  83

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  4.5.2 Tabu Search

  84

  4.5.3 Ranker

  84

  4.6 Classification Techniques

  84

  4.6.1 LDA

  86

  4.6.2 SVM

  87

  4.6.3 Fuzzy k-NN

  88

  4.7 Performance Evaluation Measurements

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CHAPTER 5: EXPERIMENTAL RESULTS AND DISCUSSION

  92

  5.1 Experimental Setup

  92

  5.1.1 Architecture of Experiments

  92

  5.1.2 Datasets for Performance Evaluation

  93

  5.1.3 Classification Techniques

  95

  5.1.4 Feature Selection Techniques

  96

  5.1.5 Performance Evaluation

  96

  5.2 Hemorrhagic Slice Classification

  97

  5.2.1 Parameter Fine-Tuning

  98

  5.2.2 Overall Results and Discussion for Hemorrhagic Slice Classification

  98

  5.2.3 Breakdown Results and Discussion for Hemorrhagic Slice 101 Classification

  5.3 Intra-axial Classification 104

  5.3.1 Parameter Fine-Tuning 104

  5.3.2 Overall Results and Discussion for Intra-axial Slice Classification 104

  5.3.3 Breakdown Results and Discussion for Intra-axial Slice Classification 106

  5.4 Subdural and Extradural Classification 108

  5.4.1 Parameter Fine-Tuning 109

  5.4.2 Overall Results and Discussion for Subdural Region and Extradural 109 Region Classification

  5.4.3 Breakdown Results and Discussion for Subdural Region and 111 Extradural Region Classification

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  5.5 Summary of Overall Three-Stage Classification Results 116

  5.6 Summary of Breakdown Classification Results 118

  5.7 Extended Experimental Results of Retrieval 119

CHAPTER 6: CONCLUSION AND FUTURE WORKS 126

  6.1 Summary of Overall Research Work 126

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  6.2 Contributions 128

  6.3 Limitations 129

  6.4 Future Works 129

  APPENDIX A 130 REFERENCES 142 PUBLICATION LIST 157

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  LIST OF TABLES Siti Hasmah Digital Library

Table 1.1 Types and Sizes of Different Modalities of Digital Medical

  2 Images

Table 2.1 Summary of the Existing Symmetric Approaches

  36 Table 2.2 Summary of the Existing Global-Based Feature Extraction

  36 Approaches

Table 2.3 Summary of the Existing Thresholding Techniques

  37 Table 2.4 Summary of the Existing Clustering or Segmentation

  38 Techniques

Table 3.1 Numerical Results of Original Image Contrast Enhancement

  60 Table 3.2 Numerical Results of Hemorrhagic Regions Contrast

  61 Enhancement

Table 5.1 Type of Slices and Their Quantities

  94 Table 5.2 The Parameter Values Set for Ranker, PSO and Tabu

  98 Table 5.3 Overall Results of the Hemorrhagic Classification by Using 100 Ranker

Table 5.4 Overall Results of the Hemorrhagic Classification by Using PSO 100Table 5.5 Overall Results of the Hemorrhagic Classification by Using 100

  Tabu

Table 5.6 Confusion Matrix

  101

Table 5.7 Breakdown Results of the Normal Slice and Hemorrhagic Slice 102 by Using RankerTable 5.8 Breakdown Results of the Normal Slice and Hemorrhagic Slice 103

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  by Using PSO

Table 5.9 Breakdown Results of the Normal Slice and Hemorrhagic Slice 103 by Using TabuTable 5.10 The Parameter Values Set for Ranker, PSO and Tabu 104Table 5.11 Overall Results of Intra-Axial Classification for Ranker 105

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Table 5.12 Overall Results of Intra-Axial Classification for PSO 106Table 5.13 Overall Results of Intra-Axial Classification for Tabu 106Table 5.14 Breakdown Results of the Non Intra Axial Slice and Intra Axial 107

  Slice by Using Ranker

Table 5.15 Breakdown Results of the Non Intra Axial Slice and Intra Axial 108

  Slice by Using PSO

Table 5.16 Breakdown Results of the Non Intra Axial Slice and Intra Axial 108

  Slice by Using Tabu

Table 5.17 The Parameter Values Set For Ranker, PSO and Tabu 109Table 5.18 Overall Results of Region Classification for Ranker 110Table 5.19 Overall Results of Region Classification for PSO 110Table 5.20 Overall Results of Region Classification for Tabu 111Table 5.21 Breakdown Results of Region Classification by Using Ranker 112Table 5.22 Breakdown Results of Region Classification by Using PSO 113Table 5.23 Breakdown Results of Region Classification by Using Tabu 114Table 5.24 Comparison Results of Twelve and Five Features 115Table 5.25 Ranked Contribution of Each Features by Ranker 115Table 5.26 Eight Features Selected by PSO and Tabu Search 116Table 5.27 Precision Obtained by Using “Hemorrhage” 124Table 5.28 Precision Obtained by Using “Intra-Axial” 125Table 5.29 Precision Obtained by Using “Extradural” 125Table 5.30 Precision Obtained by Using “Subdural” 125

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  Siti Hasmah Digital Library LIST OF FIGURES

  26 Figure 2.6 Categorization of Intracerebral hemorrhage, Reproduced from (Datta, Datta & Biswas, 2011)

  45 Figure 3.6 Contrast Enhanced Images

  45 Figure 3.5 Original Images

  45 Figure 3.4 Absolute First Difference

  43 Figure 3.3 Constructed Histogram

  42 Figure 3.2 Contrast Stretching System

  34 Figure 3.1 Flowchart for Clustering and Detection

  32 Figure 2.9 Overview of HHNN, Reproduced from (Leena, 2015)

  31 Figure 2.8 Images Showing the Gold Standard (First Column), Shrinking’s Results (Second Column) and Expansion’s Results (Third Column), Reproduced from (Bhanu et al., 2012)

  27 Figure 2.7 Detected Boundaries of the Hemorrhagic Regions, Reproduced from (Bhadauria & Dewal, 2012)

  26 Figure 2.5 Division of Sub-Regions, Reproduced from (Saito, et al., 2011)

Figure 1.1 Framework of CBIR in Medical

  24 Figure 2.4 Midline Locating from Contour, Reproduced from (Saito, et al., 2011)

  22 Figure 2.3 Seeded Region-Growing Segmentation, Reproduced from (Matesin, et al., 2001)

  19 Figure 2.2 Subarachnoid Hemorrhage Marked by An Arrow as A White Area in the Center and Stretching Out

  14 Figure 2.1 Anatomy of Brain, Reproduced from (Brain & Nervous System Health Center, 2009)

  11 Figure 1.6 Overview of Proposed Methodology

  11 Figure 1.5 Example of Extradural Hemorrhage

  10 Figure 1.4 Example of Subdural Hemorrhage

  10 Figure 1.3 Example of Intra-Axial Hemorrhage

  5 Figure 1.2 Tree Chart for the Hemorrhages’ Hierarchy

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  71 Figure 4.5 (a) Subdural Region

  90 Figure 5.1 Architecture of Experiments

  80 Figure 4.7 An Illustration for Five Nearest Neighbourhoods

  77 Figure 4.6 (a) Original Shape (b) Remodeling Shape Resulted of IDFT

  77 (h) Overlapping Area

  77 (g) Filled Up Outer Contour

  77 (f) Filled Up Inner Contour

  77 (e) Outer Closed Contour

  77 (d) Inner Closed Contour

  77 (c) Outer Contour with Located Endpoints

  76 (b) Inner Contour with Located Endpoints

  71 Figure 4.4 (a) Left Hemisphere (b) Edge Histogram

Figure 3.7 Obtained Parenchyma

  69 Figure 4.3 (a) Right Hemisphere (b) Edge Histogram

  67 Figure 4.2 Illustration for the Acquisition of the Texture Unit for 3x3 Sub- Matrix

  65 Figure 4.1 Hierarchy for the Three Annotation Processes

  63 Figure 3.14 Detected Midline by Existing Approach (First Column) and Proposed Approach (Second Column)

  61 Figure 3.13 Clustering Results by (a) Otsu Thresholding (b) FCM Clustering (c) K-Means Clustering and (d) EM Clustering

  60 Figure 3.12 Enhanced Images by Proposed Method (First Row), Histogram Equalization (Second Row) and Adaptive Histogram (Third Row)

  57 Figure 3.11 Original Images before the Contrast Enhancement

  55 Figure 3.10 (a) Contour of Parenchyma Area (b) Top Sub-Contour (c) Bottom Sub-Contour (d) Line Scanning for Local Maxima Detection (e) Shortened Searching Line (f) Located Highest Average Value of Intensity Point

  48 Figure 3.9 Illustration of Midline Acquisition by Using Midpoints

  47 Figure 3.8 Hemorrhagic Enhanced Images

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Figure 5.2 Examples of Different Kinds of Slices: (a) Intra-axial

  94 (b) Extradural (c) Subdural (d) Normal

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Figure 5.3 Summary of Recall Generated by RBF SVM for Three-Stage 117

  Classification

Figure 5.4 Summary of Precision Generated by RBF SVM for Three-Stage 117

  Classification

Figure 5.5 Twenty Five Most Relevant Retrieval Results by Keyword 120

  “Hemorrhage”

Figure 5.6 Twenty Five Most Relevant Retrieval Results by Keyword 121

  “Intra-axial”

Figure 5.7 Twenty Five Most Relevant Retrieval Results by Keyword 122

  “Extradural”

Figure 5.8 Twenty Five Most Relevant Retrieval Results by Keyword 123

  “Subdural”

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LIST OF ABBREVIATIONS

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  CBIR Content-based Image Retrieval CCDCFD Sum of Centroid Contour Distance Curve Fourier Descriptor CCR Correct Classification Rate CT Computed Tomography DICOM Digital Imaging and Communications in Medicine EM Expectation-Maximization FCM Fuzzy C-Means GL Gray Level GLCM Gray Level Co-Occurrence Matrices

  ICH Intracerebral Hemorrhage k-NN k-Nearest Neighbors LBP Local Binary Pattern LDA Linear Discriminant Analysis MRI Magnetic Resonance Image PET Positron Emission Tomography PSO Particle Swarm Optimization RBF Radial Basis Function RL Run Length ROI Region of Interest SBIR Semantics-based Image Retrieval SPECT Single Photon Emission Computed Tomography SVM Support Vector Machine TBIR Text-based Image Retrieval

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CHAPTER 1 Siti Hasmah Digital Library INTRODUCTION This chapter shows an outline of the research work. It is followed by the

  discussion on the motivation of this research work. Then, it proceeds with the descriptions of the objectives and deliverables of the research. At last, the structure of the thesis is summarized.

1.1 Research Overview

  In medical field, image serves as one of the important tools for diagnosis, treatment monitoring and management of the diseases of the patients. The use of hardcopy medical image formats such as analog screen films are decreasing. Space of storage, maintenance and film material directly contributed to the decreasing popularity. On the other hand, the usage of softcopy format of medical images is gaining its popularity as they present less of the hardcopy image problems. Besides, the digital medical images allow the digital image processing for the implementing of the automated computer-aided system. The most commonly used Digital Imaging and Communications in Medicine (DICOM) format. With DICOM, a benchmark for image communications has been established and patient data can be stored with the actual digital images. The DICOM header consists of the tags to decode the body part, patient position, scanner information and modality (NEM, 2009; Kimura et al., 2002).

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  The digital medical image databases that have been used for diagnosis, therapy and decision making come from images of various scanning likes X-ray, Computed Tomography (CT) scan, Magnetic Resonance Image (MRI), ultrasound, mammogram and so on. The overview of different modalities in terms of their sizes, types and number of images per examination is depicted in Table 1.1.

  1

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  512x512x8 15-40 4-10MB Digitized X-rays 2048x2048x12

  Ultrasound 512x512x8 20-240 5-60MB

  8MB up Nuclear medicine (NM) 128x128x12 30-60 1-2 MB

  256x256x12 60-3000

  16MB Magnetic resonance imaging (MRI)

  2

  16MB Digital radiography 2048x2048x12

  2

  0.25MB Digital subtraction angiography (DS)

  2 Table 1.1: Types and Sizes of Different Modalities of Digital Medical Images (Huang, 2004). Multi-slice CT scans are extensively utilized in today’s analysis of head traumas due to its effectiveness to unveil some abnormalities such as calcification, hemorrhage and bone fractures. In addition, it is more economical, requires shorter imaging time and possesses widespread availability (Ragavi & Nija, 2014). These

  1

  Digital microscopy 512x512x8

  0.75MB Digital mammography 4000x5000x12 4 160MB

  1

  20MB up Digital color microscopy 512x512x24

  (CT) 512x512x12 40-3000

  Color light images 512x512x24 4-20 3-15MB Computed tomography

  Examination type One image (bits) Number of image/examination One examination

  PREVIEW enable patients with size too large for MRI scanner, and patients that are unable to stay motionless due to aging or pain to perform the scanning for some diagnosis

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  purpose. For these reasons, this research work aims to explore the segmentation methods and classification methods for brain CT images.

  An enormous amount of CT images are generated in modern-day hospitals. The steady growth number of the images provides an excellent opportunity and resources for the researchers in the medical area. As such, image retrieval particularly in medical domain, becomes exciting and rapidly expanding research area. Image retrieval can be defined as a finding of similar images based on the user’s query from a significant archive with the assistance of certain key elements attached with the images or extracted features from the images. Medical image retrieval is gaining importance in the area of diagnosis, research and medical education. The crucial objective of medical image retrieval system comprises organizing, retrieving and indexing of huge collection images in extremely effectual and efficient way.

  Generally, the three main categories of medical images retrieval techniques (Henning et al., 2004; Dimitrovski et al., 2015) are Text-based Image Retrieval (TBIR), Content-based Image Retrieval (CBIR) and Semantics-based Image Retrieval (SBIR), with TBIR being the most conventional system. TBIR only provides textual information about the patients. The textual information is based on either indexing or captions that related with the images manually. Therefore, in TBIR, medical images are retrieved based on patient’s identity number, name and some other keywords manually annotated by medical expert and they are normally stored

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  in a relational database (Akbarpour, 2013; Chuctaya et al., 2011). However, TBIR experiences from some drawbacks such as the extent of effort and time needed to physically interpret each image. Furthermore, the difference in human perception while illustrating the images may cause the incorrectness in the retrieval process later on.

  3 Therefore, in the last few decades, we have CBIR. CBIR can be defined as a technique to retrieve the images based on the low-level features like color, shape,

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  texture and spatial relationship and to index the images with the minimum human involvement (Akbarpour, 2013; Akgül et al., 2011; Smeulders et al., 2000 and Rui, Huang, & Chang, 1999).

  Basically, CBIR in medical domain is to retrieve the utmost visually alike images to a provided query image from a medical database. For example, finding the brain cancer tumor images, skin images, lung images and so on from a medical image collection. The general framework of CBIR in medical domain is outlined in

Figure 1.1. Several advancements have been made in the area of medical CBIR

  (Long, Antani, Deserno, & Thoma, 2009; Müller & Deserno, 2011; Ramamurthy, Chandran, Aishwarya, & Janaranjani; 2011). Some example of ares are pathology (Zheng, Wetzel, Gilbertson, & Becich, 2003), head (Simonyan, Modat, Ourselin, Cash, Criminisi, & Zisserman, 2012), lung (Shyu, Brodley, Kak, Kosaka, Aisen, & Broderick, 1999), and mammograms (El-Naqa, Yang, Wernick, Galatsanos, & Nishikawa, 2002). However, CBIR systems do not intent to substitute the physician by predicting the disease of a specific case but to help the physician in analysis as a second opinion. The visual features of a disease contain diagnostic data and often times visually alike images relate to the same disease group. By referring the outcome of a CBIR system, the physicians can obtain more assurance in his/her conclusion or even can mull over other possibilities.

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  4

  Siti Hasmah Digital Library Figure 1.1: Framework of CBIR in Medical

  The other group of image retrieval system, SBIR has emerged since the early 2000’s. The basic idea of semantics-based is to retrieve the images based on PREVIEW keywords. Both SBIR and TBIR exploit the similar method to the image retrieval which is by using the keywords for the retrieval. However, TBIR needs human assistance in annotation of each image while in SBIR, images are automatically annotated. In SBIR, the principal objective is to acquire the semantics of the images, by way of automatic image annotation. In medical field, semantic textual labels are attached to the images such as hemorrhage, infarct and so on (Kalpathy-Cramer & Hersh, 2007). In order to obtain the semantic textual label, images are segmented

  5