AUTOMATIC DETECTION OF DIABETIC RETINOPATHY INCLUDING NEOVASCULARIZATION BASED ON

MORPHOLOGICAL OPERATIONS

~

Siti Syafinah Binti Ahmad Hassan

Master of Engineering 2012

'usa. KhiOmal Ma mal A a.emit-. UNIVERSm MALAVSIA SARAWAK

(

AUTOMATIC DETECTION OF DIABETIC RETINOPATHY INCLUDING NEOV ASCULARIZATION BASED ON

MORPHOLOGICAL OPERATIONS

SITI SYAFINAH BINTI AHMAD HASSAN

A thesis submitted in fulfillment of requirements for the degree of Master of Engineering

"1.

F acuity of Engineering UNIVERSITI MALAYSIA SARAW AK

2011

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DECLARATION

I would like to declare that this dissertation is my original writing, except the data, the notes

and fact that already stated with its sources and origins.

~-

SIT! SY AFINAH BINT! AHMAD HASSAN 850909-13-5830

:For he{ovedMOM and1J.Jt1J

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ACKNOWLEDGEMENTS

I am heartily thankful to my supervisor, Ir. David Bong Boon Liang for his encouragement,

guidance and support from the initial. to the final level that enabled me to develop the project.

Special thanks are given to my co-supervisor which also acts as consultant ophthalmologist

Dr. Maflika Premsenthil for her kind supervision and provision of medical expertise. I am

also thankful to Local Hospital and all the publically available databases (DRIVE, STARE,

" DiaretdbO arrd Messidor program) for providing the diabetic retinal images and clinical

infonnation. It is a pleasure to thank Ministry of Science, Technology and Innovation,

Malaysia for the financial support on this project.

Lastly, I offer my regards and blessings to all of those who supported me in any respect

during the completion of the project especially my parents for their loving support and

encouragement in so many ways. I am sure the success of this project would make them

proud. I cannot thank enough my special friend for his love and support, without which I

would never have completed this work.

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ABSTRACT

("Diabetic retinopathy is a widely spread eye disease caused by diabetes complication.

Screening to detect retinopathy disease can lead to successful treatments in preventing

blindness especially at early stages. An automated decision support system for the purpose of

detecting and classifying retinal abnormalities is carried out mainly using an image processing

methods as presented in this thesis. The retinal images are automatically analyzed in term of

pixel-based and region-based diagnostics accuracies after compared with ophthalmologist's

hand-trut~ An adjusted morphology-based, thresholding and mathematical-based pixel

segmentation methods are developed to segment the bright lesions from background and to

distinguish from other retinal feature especially optic disc. Gradient classifier has been used to

distinguish hard exudates and cotton wool spot from bright lesions segmentation result. The

preliminary pixel-based hard exudates and pixel-base cotton wool spot analysis are used to

support the fact that development of a reliable retinal abnormalities identification system is

feasible. There are small dark lesions and large dark lesions detection methods presented in

this thesis. Image enhancement, image restoration, morphology operator, thresholding and

compactness properties techniques have been used in development of automatic dark lesions

detection system. Lastly, neovascularization lesion identification is still a new study in

automatic detection of diabetic retinopathy. Detection of neovascularization is important since

it signifies the disease has reaches a vision-threatening phase. Therefore, image normalization,

morphology-based operator, Gaussian filtering and thresholding techniques are used in

developing of neovascularization detection. Moreover, a function matrix box-based on

predefined criteria of neovascularization has been used in order to classify the

neovascularization from natural blood vessel. The developed method is tested on a set of 303

IV

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images from different database sources to classify the images as abnormal or normal images.

The proposed method managed to achieved 90.29% score for abnormal images and 100%

score for normal images. Result after testing shows 85.39% sensitivity and 94.59% specificity

for bright lesions, 70.68% sensitivity and 99.22% specificity for dark lesions and 63.9%

sensitivity and 89.4% specificity for neovascularization. Further detail of lesions shows

61.24~ sensitivity and 98.43% specificity for hard exudates, 48.62% sensitivity and 98.75%

specificity for cotton wool spots, 27.41% sensitivity and 99.94% specificity for

microaneurysms and 67.93% sensitivity and 99.70% specificity for haemorrhages.

Keywords: Diabetic retinopathy, dark lesions, bright lesions, hard exudates, cotton wool

spots, neovascularization, image processing.

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II,....... ,­

ABSTRAK

Diabetik retinopati adalah penyakit mata yang semakin berleluasa djsebabkan oleh komplikasi

diabetes.; Pengujian untuk mengesan penyakit retinopati membolehkan rawatan awal

dilakukan untuk mengelak kerosakan penglihatan pada peringkat awal. Sistem sokongan

automatik ini bertujuan mengesan dan mengkategorikan abnormal retina menggunakan

kaedah pemprosesan imej seperti diterangkan dalam tesis ini. Imej retina dianalisa secara

automatik dari segi ketepatan diagnosis kawasan dan piksel selepas perbandingan dengan

lakaran oftalmologi. Pengolahan berasaskan morfologi, proses pembahagian dan kaedah

matematik berasaskan segmen piksel telah dibina untuk memisahkan bright lesions daripada

latar belakang dan membezakan dari ciri-ciri retina yang lain terutamanya cakera optik.

Pengelasan berdasarkan kecerunan telah digunakan untuk membezakan hard exudates dan

cotton wool spot daripada keputusan segmentasi bright lesions. Analisa asas berdasarkan

piksel untuk hard exudates dan cotton wool spot dapat digunakan untuk menyokong

kenyataan bahawa pembangunan sistem pengesanan ketidaknormalan retina yang mempunyai

keboleharapan yang tinggi adalah munasabah. Terdapat kaedah pengesanan dark lesions yang

keeil dan besar ditunjukkan dalam tesis ini. Penambah baik imej, pembaharuan imej, operasi

morfologi, proses pembahagian dan teknik ciri-ciri kepadatan telah digunakan dalam

membangunkan sistem pengesanan automatik dark lesions. Akhir sekali, pengesanan simtom

neovascularization merupakan satu kajian yang masih baru dalam pengesanan automatik

diabetik retinopati. Pengesanan neovascularization sangat penting kerana ia menunjukkan

penyakit sudah mencapai fasa rosak penglihatan. Oleh itu, penyeragaman imej operasi asas

morfoiogi, penapisan Gaussian dan proses pembahagian digunakan dalam membangunkan

sistem pengesanan ciri-ciri neovascularization. Selanjutnya, fungsi kotak matrik berasaskan

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kriteria yang telah ditentukan digunakan untuk pengkelasan neovascularization dari salur

darah yang normal. Kaedah yang dibangunkan telah diuji dengan menggunakan 303 imej dari

pelbagai sumber untuk pengkelasan imej normal dan imej tidak normal. Kaedah ini berjaya

mencapai keputusan 90.29% markah bagi imej tidak normal dan 100% markah bagi imej

normal. Keputusan selepas pengujian kaedah menunjukkan 85.39% sensitif dan 94.59%

spesifik bagi bright lesions, 70.68% sensitif dan 99.22% spesifik bagj dark lesions dan

58.95% sensitif dan 88.03% spesifik bagi neovascularization. Lesions yang lebih terperinci

menunujukkan 61 .24% sensitif dan 98.43% spesifik bagi hard exudates, 48.62% sensitif dan

98.75% spesifik bagi cotton wool spots, 27.41 % sensitif dan 99.94% spesifik bagi

microaneurysms dan 67.93% sensitif dan 99.70 spesifik bagi haemorrhages.

Kata kunci: Diabetik retinopati, dark lesions, bright lesions, hard exudates, cotton wool

spots, neovascularization, pemprosesan imej .

VII

Pusat Kltidmat Maldumat Akademik UNlVERSm MALAYSIA SARAWAJ(

TABLE OF CONTENTS

Page

ACKNOWLEDGEMENTS III

ABSTRACT IV

ABSTRAK VI

TABLE OF CONTENTS viii

LIST OF FIGURES x

LIST OF TABLES Xlll

LIST OF ABBREVIATIONS XIV

CHAPTER 1: INTRODUCTION 1. ] Background 1 1.2 Problem statements 3 1.3 Scope and objectives 4 1.4 Research overview 5 1.5 Thesis structure 6

CHAPTER 2: DETECTION OF RETINAL ABNORMALITIES USING IMAGE PROCESSING 2.1 Introduction 7 2.2 Structure of the eye 7 2.3 Diabetic retinopathy background 9

2.3.1 Diabetic retinopathy complications 10 2.3.2 Diabetic retinopathy risk factor and preventions 15 2.3.3 Screening for diabetic retinopathy 16

2.4 Computer vision fundamental 19 2.4.1 Image acquisition 21 2.4.2 Image pre-processing 21 2.4.3 Image segmentation 24

2.5 Computer vision in related diabetic retinopathy studies 27

CHAPTER 3: METHODOLOGY 3.1 Introduction 34 3.2 Image acquisition 36 3.3 Bright lesions detection 38

3.3.1 Pre-processing for retinal image 40

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l 3.3.2 Bright lesions segmentation 42 3.3.3 Bright lesions classification 58 3.3.4 Bright lesions quantification 64

3.4 Dark lesions detection 65 3.4.1 Dark lesions segmentation 68 3.4.2 Dark lesions classification 77 3.4.3 Dark lesions quantification 83

3.5 Neovascularization detection 84 3.5.1 Neovascularization segmentation 88 3.5.2 Neovascularization classification 88

3.6 Abnormal and normal classification 90

CHAPTER 4: RESULTS AND DISCUSSION 4.1 Introduction 92 4.2 Sensitivity and Specificity analysis 92

4.2.1 Bright lesions 93 4.2.2 Dark lesions 98 4.2.3 Neovascularization lesions 101

4.3 Receiver operating characteristic (ROC) Graph 103 4.3.1 Bright lesions 104 4.3.2 Dark lesions 107 4.3.3 Neovascularizations 110

4.4 Classification of normal and abnormal images 112 4.5 Comparative study 112 4.6 Discussion 118

CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS 5.1 Introduction 121 5.2 Conclusions 121 5.3 Recommendations 126

REFERENCES

APPENDIX A: Bright lesions detection - SN and SP for 256 test images 135 139 151 155 167 167 172 179 196 204

APPENDIXB: Test result of automatic bright lesion detection APPENDIXC: Dark lesions detection - SN and SP for 256 test images APPENDIXD: Test result of automatic dark lesion detection APPENDIX E: Neo detection - SN and SP for 9 test images APPENDIX F: Test result of automatic neo detection APPENDIXG: Images summary APPENDIXH: Algorithm for the proposed research APPENDIX I: Journal of Digital Imaging Springer (2011) APPENDIXJ: National Conference on Engineering and

APPENDIXK:

127

Technopreneurship (NCET 2010) Proceedings of EN CON 2010 209

IX

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Figure

Figure 1.1: 1

Figure 2.1:

Figure 2.2: Figure 2.3: Figure 2.3: Figure 2.4: Figure 2.5: Figure 2.6: Figure 2.7: Figure 2.8: Figure 2.9:

Figure 2.10: Figure 2.11 : Figure 2.12: Figure 3.1: Figure 3.2: Figure 3.3:

Figure 3.4:

Figure 3.5:

Figure 3.6: Figure 3.7:

I' Figure 3.8:

Figure 3.9:

Figure 3.10: Figure 3.11:

Figure 3.12:

LIST OF FIGURES

Title Page

Outline of the proposed system for automatic detection of retinal 6 abnormalities Structure of eye; (a) cross sectional view of human eye; (b) A 8 digital photograph of the retina or fundus image Microaneurysms 13 (a) Blot and dot haemorrhages; 13 (b) Flame haemorrhages 14 Hard exudates 14 Cotton wool spot 15 Neovascularization 15 A 2x2 contingency table 19 Fundamental steps in digital image processing 20 Example of spatial filtering; (a) Original image; (b) Result of noise 22 reduction with 3x3 mean filter; (c) Result of noise reduction with 3x3 median filter Gray - scale transformations 23 The Dilation operation 26 The Erosion operation 26 The main stage in automatic detection of retinal abnormalities 35 Screening of diabetic retinopathy using fundus camera 38 The outline of the proposed system for automatic identification of 39 retinal bright lesions. The process for bright retinal lesions segmentation of the proposed 43 system (a) Green channel image; (b) Morphology dilation image; (c) 46 Morphology erosion for image (b) Window size 5x5 47 Four orientations in window; (a) 0 degree; (b) 45 degree; (c) 90 48 degree; (d) 135 degree (a) mean difference value = I; (b) mean difference value = 3; (c) 50 mean difference value = 5; (d) mean difference value = 7; (e) mean difference value = 10 (a) Identification of midpoint; (b) After thinning; (c) After flood-fill 51 operation; (d) After morphological opening operation The process for optic disc localization of the proposed system 53 The procedure for bright retinal feature extraction and region 58 classification of the proposed system (a) Reconstructed image of the pre-processed image; (b) Bright 60 lesions region obtained after thresholding the result image of subtraction between (a) and pre-processed image

x

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I r

Figure 3.13:

Figure 3.14: Figure 3.15:

Figure 3.16:

Figure 3.17: Figure 3.'18:

Figure 3.19: Figure 3.20: Figure 3.21: Figure 3.22 Figure 3.23: Figure 3.24:

Figure 3.25:

Figure 3.26:

Figure 3.27: Figure 3.28: Figure 3.29: Figure 3.30:

Figure 3.31: Figure 3.32:

Figure 3.33:

Figure 3.34:

Figure 3.35:

Figure 3.36:

Figure 3.37: Figure 4.1:

Figure 4.2:

Figure 4.3:

Figure 4.4:

The procedure for advance bright lesions classification to 61 distinguish between hard exudates and cotton wool spots of the proposed system (a) Hard exudates; (b) Cotton wool spots 63 The procedure for quantifying number of hard exudates and cotton 64 wool spots that signify occurrences of Diabetic Retinopathy The outline of the proposed system for automatic identification of 67 retinal dark lesions The procedure for dark lesions segmentation of the proposed system 69 Block diagram of dark expose function for dark segmentation Contrast Enhancement; (a) , imadjust'; (b) , adjcontrast' Dilation Erosion 'dark expose' result image Image subtraction

lesions 70

71 72 73 73 74

Block diagram of blood vessel extraction function for dark lesion 76 segmentation (a) Thresholding; (b)Morphology open filter; (c) Compactness 77 classifier Flow chart for dark retinal feature extraction and reglOn 79 classification of the proposed system Morphology fill; (a) Image subtraction; (b) Small dark lesions 80 (a) Early subtraction; (b) After morphology open 81 Fovea 82 The procedure for advance dark lesions classification to distinguish 82 between microaneurysms and haemorrhages of the proposed system Total dark lesion 83 The procedure for quantifying number of microaneurysms and 84 haemorrhages that signify occurrences of Diabetic Retinopathy The outline of the proposed system for automatic identification of 85 retinal neovascularization lesions (a) Image after applying pre - processing operation; (b) Image after 87 applying match - based filter; (b) Contrast stretched image; (b) Image after mUltiple morphology dilation and erosion; (b) Image subtraction between (c) and (d); (f) Morphology opening The procedure for Neovascularization segmentation of the proposed 88 system The procedure for Neovascularization feature extraction and region 89 classification of the proposed system Neovascularization classification result 90 Samples of results comparing the bright lesions reglOn with 97 ophthalmologist's hand-truth Samples of results comparmg the dark lesions region with 99 ophthalmologist's hand-truth Samples of results comparing the neovascularizations region with 102 ophthalmologist's hand-truth Comparing ROC Curves 104

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I Figure 4.5: ROC curve for bright lesion detection 107

Figure 4.6: ROC curve for dark lesion detection 110

Figure 4.7: ROC curve for Neovascu1arization detection 111

XII

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LIST OF TABLES

Table Title Page

Table 2.1: I

International clinical Diabetic Retinopathy Disease Severity scale 11 Table 2.2: Screening follow up schedule of Diabetic Retinopathy stage 17 Table 4.1: The result of bright lesion detection in 47 retinal image that used in 95

developing the proposed algorithm Table 4.2: The result of dark lesions detection in 47 retinal image that used in 100

developing the proposed algorithm Table 4.3: The result of neo detection in 3 retinal image that were used in 101

developing the proposed algorithm Table 4.4: Table of TP and FP used to construct the ROC curve of bright 106

lesions detection generated by online software Table 4.5: Table of TP and FP used to construct the ROC curve of dark lesions 109

detection generated by online software Table 4.6: Table of TP and FP used to construct the ROC curve of 110

neovascularization detection generated by online software Table 4.7: Comparison of automated detection of diabetic retinopathy 117 Table 4.8: Sensitivity and specificity result in developing the proposed system 119 Table 4.9: Sensitivity and specificity result in testing the proposed system 119 Table 4.10: Summary result of abnormal and normal image 119

XliI

DR

NPDR

PDR

IRMA

SN

SP

TP

IN

FN

FP

ROC

FOV

NEO

LIST OF ABBREVIATIONS

Diabetic Retinopathy

Non.proliferative Diabetic Retinopathy

Proliferative Diabetic Retinopathy

Intra Retinal Microvascular Abnonnalities

Sensitivity

Specificity

True Positive

True Negative

False Negative

False Positive

Receiver Operating Characteristic

Field of view

Neovascularization

XIV

CHAPTERl

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INTRODUCTION

1.1 BACKGROUND

This thesis explores the identification of diabetic eye disease in digital images of human

eye. Image processing techniques are introduced to detect and classify picture element of

digital retinal images into diabetic eye lesion classes.

The rising cases of diabetes mellitus give great challenges to public health care. Diabetes

prevalence rate in Malaysia has risen much faster than expected, almost doubling in

magnitude over the last decade (Zanariah, H. et at. 2006). International Diabetes Federation

(lDF) predicted that by the year 2025, the South East Asian Region would have the highest

prevalence of diabetes. World Health Organization (WHO) also has estimated that by the year

2030, Malaysia would have a total of 2.48 million people with diabetes (Zanariah, H. et al.

2006).

The First National Health and Morbidity Survey (NHMS I) conducted in 1986 had

reported a prevalence of diabetes of 6.3% and in the Second National Health and Morbidity

Survey in 1996 (Maimunah, A.H, 1996), this had risen to 8.3%. The Third National Health

and Morbidity Survey (NHMS III) in 2006 shows the prevalence of diabetes is 14.9% among

the population aged 30 years and above. Most of the patients are not aware of the diabetic

progression until it gets worse. Therefore, late treatment of diabetic disease will consequently

leads to various complications especially retinopathy.

Diabetic retinopathy (DR) is the most common cause of blindness in adults of working

age in Malaysia. The diabetic retinopathy stage varies with the duration of disease. At

diagnosis, less than 5% of the patients have retinopathy while after 10 years the prevalence

rate has risen to 40-50%. After 20 years, almost all patients with type I diabetes and more than

60% patients with type II diabetes have some degree of retinopathy (Jaudin, R. 2006).

The National Eye Survey that was carried out in 1996 showed that the prevalence of

diagnosed diabetes in Malaysia aged 50 years and above was estimated to be 10.3% or about

200,000 people and 3.5% of them might have diabetic retinopathy. Thus, about 7,300 people

aged 50 years and above were estimated to have diabetic retinopathy in Malaysia. Diabetic

retinopathy has become an increasingly important cause of blindness. Nevertheless, vision

loss can be prevented from early detection of diabetic retinopathy and monitor the existing

retinopathy with regular examination (Jaudin, R. 2006).

Most of the patients are being treated at the primary care level-general practitioner clinics,

public out-patient clinics and health centers. The huge number of patients at these primary

care clinics is in sharp contrast to the limited number of expert ophthalmologists that need to

sereen and review the retinal images of the patients. A study by Javitt, J.C. (1989) and Jaudin,

It (2006) showed that the high cost of screening and shortage of medical professionals are the

fictors that hamper patients from taking regular screening.

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With the advancement of digital image processing technologies, the techniques can be

applied to analyze retinal images in the absence of expert ophthalmologists. The automatic

diagnosis and analysis techniques can help ophthalmologists in diabetic screening for

detecting the symptoms faster, more easily and reduce ophthalmologist workload especially in

developing country where the number of experts is not sufficient to deal with the large

number of diabetic retinopathy patients.

The research work in this thesis aims to automatically diagnose abnormalities in retinal

images and further analyze the abnormalities. This automated process would speed up

diagnosis of retinopathy, thus cases which require urgent attention could be referred

immediately to an expert for further evaluations.

1.2 PROBLEM STATEMENTS

A regular eye examination done by an expert ophthalmologist is a manual way in

diagnosing and quantifying retinal abnormalities of each patient. Consequently, this manual

method increases the expert workload and they will be inclined to spend less time to diagnose

one retinal image, thus higher tendency of error in diagnosis. Most of the previous researches;

I

Walter, T. et al. (2002), Sopharak, A. and Uyyanonyara, B. (2006), Niemeijer, M. et al.

(2005), Wang, H. et al. (2000), Sinthanayothin, C. et al. (2002), Hipwell, J.H. et al. (2000), Li,

H. and Chutatape, O. (2000), Osareh, A. (2002), Ege, B.M. et al. (2000), Usher, D. et al.

(2004) and Abramoff, D.M. et al. (2008) focused on detecting selected lesions as will be

lbrther explained in Section 2.4.2. This does not provide useful data on the performance of

'IDtomatic detection of retinal abnormalities. The related work by Zhang, L. et al. (2009)

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1.3

shows an improved detection of blood vessel which is able to include neovascularization net.

However, the work did not classify the neovascularization from normal blood vessel.

The clinical eye problem is however different from the experimental situation. Usually, a

patient will have more than one type of lesion. Thus, to focus on detecting a selected lesion is

not an appropriate approach if the detection method is to be applied clinically. This research

aims to simulate the clinical problem condition by having more than one lesion in the retinal

image. This approach is important as it will allow researchers to observe the effect of each

parameter set in the algorithm dynamically, because the number of lesions is not fixed as one.

Only a few researchers did the study on neovascularization that is the advanced

proliferative diabetic retinopathy condition. This condition is much severe than having only

dark lesions or bright lesions as it involves branches of blood vessels that could lead to

bleeding and blindness. This research includes an attempt to detect neovascularization in the

retinal image.

SCOPE AND OBJECTIVES

J

The focus of this research is to contribute a reliable image processing method for

automatic detection of diabetic retinal abnormalities. A limitation for this research is the

standardization of the database cannot be realized due to various specification of fundus

camera used. This research obtains the retinal images from several website and also from a

local hospital. Thus, the database contains different image resolution, angle of lens and quality.

4

Pusat Khidmat Maklumat Akadt~ UNIVERSm MALAYSIA SARAWAK

It affects and lower the performance of the proposed algorithm as the algorithm needs to cater

with the differences from various images.

The objectives of this research are:

i. Identify the progression of retinopathy in eyes and the symptoms.

11. To develop a method for automated detection of diabetic retin

fundus photographic with adaption to image quality.

iii. To diagnose and classify dark retinal, bright retinal and

abnormalities as an abnormal and normal retinal images

opathy in digitized

neovascularization

1.4 RESEARCH OVERVIEW

The overall structure of the methods used in this research is shown in Figure 1.1. The

input colour retinal image or fundus image is analyzed automatically and produces an

assessment of abnormalities characteristic. The proposed method will then be able to identify

and quantify the characteristic into particular classifications.

Detection of retinal abnormalities in this research is classified into microaneurysms and

haemorrhages as dark lesions, hard exudates and cotton wool spots as bright lesions and

aeovascularizations. However, detection and classification of neovascularization abnormality

bas not been explored widely compared to dark and bright lesions. Therefore, this research is

III attempt to explore the advanced proliferative diabetic retinopathy condition by using

digital image processing. The result of obvious detected retinal abnormalities will be used to

classify the nonnal or abnormal retinal images.

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Input Image \

+ .. J I Bright lesions

f Dark lesions Neovascularization I

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Figure 1.1 : Outline of the proposed system for automatic detection of retinal abnonnalities.

1.5 THESIS STRUCTURE

The thesis consists of five chapters and is organized as follows: Chapter 2 introduces the

Rtina structure and its complication, i.e. diabetic retinopathy background, symptoms, causes

treatment. This chapter also provides literature review on retinopathy techniques. Chapter

describes the retinal abnonnalities detection methodology such as image acquisition, pre­

_~;sing. segmentation and classification of retinal abnonnalities with the proposed image

_~lSinlg method. Chapter 4 reports analysis of the proposed method in tenns of sensitivity

specificity together with graph of ROC (receiver operating characteristic) for the retinal

available in this research. Chapter 5 presents overall conclusion of this work, its

lllievemelnt and future investigations.

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! Abnonnalities

detection

1 I

Classification

I

1 Abnonnal / Nonna!

CHAPTER 2

DETECTION OF RETINAL ABNORMALITIES USING IMAGE PROCESSING

2.1 INTRODUCTION

In this chapter, the structure of retina will be introduced especially the main retinal

components and retinal abnormalities. The prospective of diabetic retinopathy in terms of

disease progression, symptoms, causes and the treatments also will be discussed in this

section. Furthermore, previous works of automatic retinal image analysis are also reviewed in

this research as a guideline for developing the proposed method.

2.1 STRUCTURE OF THE EYE

Diabetic complication associated with the eye is called diabetic retinopathy disease.

Diabetic retinopathy occurs in the retina with the progression of abnormalities in the eye.

Figure 2.1(a) shows the cross sectional view of human eye and the image was taken from

National Eye Institute, 2008. The cornea is a see-through layer at the front of the eye and acts

like a window. Iris is responsible for the colour part of the eye. Pupil is located in the middle

of iris which controls the amount of light that enters the eye. Lens are located behind the

comea, transparent and shaped like a disc. Lens control the focus of the eye. Optic nerve is

responsible to deliver messages from the retina to the brain. Vitreous gel is a clear gel that

7

fills the space between the lens and the retina. The retina is a light sensitive part inside the

inner layer of the eye. It is responsible to turn light into image signal that the brain can

understand.

(a) (b)

Figure 2.1: Structure of eye: (a) Cross sectional view of human eye (b) A digital photograph of the retina or fundus

Image

For the research work presented in this thesis. the retina is the most important part of the

. The retina acts like a tilm as shewn in Figure 2.1 (b) and the image was taken from

J

lainen. M. 2005. Once the light reaches the retina, impulses are created and sent via

'c disc nerve to the brain. The retina colour is normally reddish due to the blood supply

retina. Optic disc. macula and blood vessel are the main anatomical structures in retina.

8