Project

Automatic Exudates Detection From Diabetic Retinopathy, Retinal Image Using Fuzzy C-Means &Morphological structure

1 Department of Biomedical Engineering,KLECET,Belgaum

2012

Automatic Exudates Detection From Diabetic Retinopathy, Retinal Image Using Fuzzy C-Means &Morphological structure

ABSTRACT Diabetic-related eye disease is a major cause of preventable blindness in the world. It is a complication of diabetes which can also affect various parts of the body. When the small blood vessels have a high level of glucose in the retina, the vision will be blurred and can cause blindness eventually. This is known as diabetic retinopathy. Regular screening is essential in order detect the early stages of diabetic retinopathy for timely treatment to prevent or delay further deterioration. This project detects the presences of abnormalities in the retina such as the structure of blood vessels, microaneurysms, exudates and texture properties using image processing techniques. These features are input into artificial neural network for automatic detection and can quickly process a large number of fundus images obtained from mass screening to help reduce the cost and increase productivity and efficiency for ophthalmologists.

2 Department of Biomedical Engineering,KLECET,Belgaum

2012

Automatic Exudates Detection From Diabetic Retinopathy, Retinal Image Using Fuzzy C-Means &Morphological structure

TABLE OF CONTENTS ACKNOWLEDGEMENTS............................................................................................. I CHAPTER 1. INTRODUCTION..................................................................................1 1.1 BACKGROUND OF PROJECT...................................................................................................................................1 1.2 CLASSIFICATION OF DIABETIC RETINOPATHY...........................................................................................3 1.2.1 NON-PROLIFERATIVE DIABETIC RETINOPATHY........................................................................................3 1.2.2 PROLIFERATIVE DIABETIC RETINOPATHY..................................................................................................5 CHAPTER 2. MATERIALS AND METHOD...................................................................6 2.1 2.2 2.3 2.4 2.5

PROJECT OBJECT....................................................................................................................................................6 OVERALL OBJECT...................................................................................................................................................6 PROJECT APPROACH...............................................................................................................................................7 SKILLS REQUIRED....................................................................................................................................................8 SOFTWARE SPECIFICATION...................................................................................................................................8

CHAPTER 3. MATLAB FUNCTIONS...........................................................................9 3.1 IMAGE ANALYSIS AND ENHANCEMENT...............................................................................................................9 3.2 MORPHOLOGICAL OPERATIONS........................................................................................................................11 CHAPTER 4. FEATURES EXTRACTION....................................................................12 4.1 OVERALL BLOCK DIAGRAM..............................................................................................................................12 4.2 BLOOD VESSELS DETECTION.............................................................................................................................13 4.2.1 INTRODUCTION..................................................................................................................................................13 4.2.2 EXPERIMENTAL PROCEDURE - DETECTION OF BLOOD VESSELS...............................................................14 4.2.3 EXPERIMENTAL PROCEDURE - BORDER FORMATION................................................................................17 4.2.3.1 BORDER FORMATION METHOD 1.......................................................................................17 4.2.3.2 BORDER FORMATION METHOD 2.......................................................................................18 4.2.4 EXPERIMENTAL PROCEDURE - MASK CREATION FOR OPTICAL DISK....................................................19 4.2.5 EXPERIMENTAL PROCEDURE - AND LOGIC................................................................................................20 4.2.6 RESULTS........................................................................................................................................................21 4.2.7 ANOVA TEST RESULTS............................................................................................................................21 4.2.8 DISCUSSION...................................................................................................................................................22 4.3 EXUDATES DETECTION.......................................................................................................................................23 4.3.1 INTRODUCTION..................................................................................................................................................23 4.3.2 EXPERIMENTAL PROCEDURE - DETECTION OF EXUDATES.......................................................................24 4.3.3 EXPERIMENTAL PROCEDURE - AND LOGIC................................................................................................27 4.3.4 RESULTS.............................................................................................................................................................28 4.3.5 ANOVA TEST RESULTS............................................................................................................................28 4.3.6 DISCUSSION...................................................................................................................................................29 4.4 MICROANEURYSMS DETECTION........................................................................................................................30 4.4.1 INTRODUCTION..................................................................................................................................................30 4.4.2 EXPERIMENTAL PROCEDURE - DETECTION OF MICROANEURYSMS........................................................31 4.4.3 RESULTS.............................................................................................................................................................34 4.4.4 ANOVA TEST RESULTS............................................................................................................................34 4.4.5 DISCUSSION...................................................................................................................................................35 4.5 TEXTURE IDENTIFICATION..................................................................................................................................36 4.5.1 INTRODUCTION..................................................................................................................................................36 4.5.2 EXPERIMENTAL PROCEDURE - ENTROPY FUNCTION..................................................................................37 4.5.3 EXPERIMENTAL PROCEDURE - HOMOGENEITY...........................................................................................38 4.5.4 ANOVA TEST RESULTS............................................................................................................................39 4.6 SUMMARY OF FEATURES EXTRACTION...................................................................................................40 4.7 OVERALL ANOVA TEST RESULTS.........................................................................................................41

3 Department of Biomedical Engineering,KLECET,Belgaum

2012

Automatic Exudates Detection From Diabetic Retinopathy, Retinal Image Using Fuzzy C-Means &Morphological structure

2012

CHAPTER 5. ARTIFICIAL NEURAL NETWORK......................................................................................42 5.1 5.2 5.3 5.4 5.5 5.6 5.7

INTRODUCTION.....................................................................................................................................................42 TRAINING OF ANN........................................................................................................................43 FEATURES DATA FOR ANN...........................................................................................................45 TRAINING DATA FOR ANN............................................................................................................46 TEST DATA FOR ANN....................................................................................................................48 SENSITIVITY AND SPECIFICITY...........................................................................................................................49 DISCUSSION...........................................................................................................................................................50

CHAPTER 6. GRAPHIC USER INTERFACE................................................................51 6.1 INTRODUCTION.....................................................................................................................................................51 6.2 DESIGN AND APPLICATION OF GUI................................................................................................52 CHAPTER 7.

CONCLUSIONS .................................................................................53

CHAPTER 8. RECOMMENDATIONS........................................................................54 REFERENCE LIST.....................................................................................................55 APPENDICES...........................................................................................................56 A1. A2. A3. A4. A5. A6. A7. A8.

MATLAB CODE FOR DETECTING BLOOD VESSELS........................................................................................57 MATLAB CODE FOR DETECTING EXUDATES..................................................................................................60 MATLAB CODE FOR DETECTING MICROANEURYSMS..................................................................................63 MATLAB CODE FOR TEXTURE ANALYSIS - ENTROPY.................................................................................66 MATLAB CODE FOR TEXTURE ANALYSIS - HOMOGENEITY.......................................................................67 MATLAB CODE FOR TRAINING ANN............................................................................................68 MATLAB CODE FOR TESTING ANN..............................................................................................69 MATLAB CODE FOR GUI..............................................................................................................70

LIST OF FIGURES....................................................................................................73

Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure

1.1 Normal Vision.........................................................................................1 1.2 Vision of a person with diabetic retinopathy............................................1 1.3 Example of a fundus camera....................................................................2 1.4 Cross sectional view of the human eye....................................................3 1.5 Fundus Image..........................................................................................4 1.6 Retinal fundus images of different stages of diabetic retinopathy............5 3.1 Original image.........................................................................................9 3.2 Contours of the image after edge detection..............................................9 3.3 Image before (Left) and after applying image segmentation (Right)...10 3.4 Results of morphological operations........................................................11 4.1 Block Diagram of Overall System...........................................................12 4.2.1 Block Diagram for Blood Vessels Detection........................................13 4.2.2 Original fundus image..........................................................................14 4.2.3 Inversed image after histogram equalization........................................14 4.2.4 Image after Morphological opening results..........................................14 4.2.5 Image after optical disk removed.........................................................14 4.2.6 Binary image after Image segmentation...............................................15 4.2.7 Blood vessels after removal of “noise”................................................15 4.2.8 Image after histogram equalization......................................................15 4.2.9 Blood vessels after image segmentation and removal of “noise”.........15 4.2.10 Mask at the optical disk area..............................................................16 4.2.11 Combined image after removing “noise”..........................................16 4.2.12 Image of Blood vessels......................................................................16

4 Department of Biomedical Engineering,KLECET,Belgaum

Automatic Exudates Detection From Diabetic Retinopathy, Retinal Image Using Fuzzy C-Means &Morphological structure

2012

Figure 4.2.13 Block Diagram for Border Formation - Method 1.............................17 Figure 4.2.14 Block Diagram for Border Formation - Method 2.............................18 Figure 4.2.15 Example of Circular border obtained using either method.................18 Figure 4.2.16 Comparing blood vessels images obtained........................................20 Figure 4.2.17 Blood vessels image after apply AND logic......................................20 Figure 4.2.18 Fundus image (Left) with its blood vessels image (Right).................21 Figure 4.3.1 Block Diagram for Exudates Detection................................................23 Figure 4.3.2 Original fundus image..........................................................................24 Figure 4.3.3 Intensity adjusted grayscale image.......................................................24 Figure 4.3.4 Image after Morphological closing......................................................24 Figure 4.3.5 Image after column filter......................................................................24 Figure 4.3.6 Mask for the Optical Disk....................................................................25 Figure 4.3.7 Image with Optical Disk removed........................................................25 Figure 4.3.8 Regions of exudates.............................................................................25 Figure 4.3.9 Image after Morphological closing......................................................25 Figure 4.3.10 Image with Dark features (represented as white)...............................26 Figure 4.3.11 Exudates after performing ‘AND’ logic............................................26 Figure 4.3.12 Expanded exudates regions after Morphological closing...................27 Figure 4.3.13 Dark features represented as white....................................................27 Figure 4.3.14 Fundus image (Left) with its exudates image (Right)........................28 Figure 4.4.1 Block Diagram for Microaneurysms Detection....................................30 Figure 4.4.2 Original fundus image..........................................................................31 Figure 4.4.3 Intensity adjusted grayscale image.....................................................................................31 Figure 4.4.4 Image after histogram equalization...........................................................31 Figure 4.4.5 Image of edges..........................................................................................31 Figure 4.4.6 Image after function “imfill”.....................................................................32 Figure 4.4.7 Image after removing the larger area.........................................................32 Figure 4.4.8 Image after applying histogram equalization twice...................................32 Figure 4.4.9 Image after image segmentation................................................................32 Figure 4.4.10 Blood Vessels after removing small area of noise..................................33 Figure 4.4.11 Microaneurysms.....................................................................................33 Figure 4.4.12 Fundus image (Left) with its microaneurysms image (Right)................34 Figure 4.5.1 Block Diagram for Texture Identification.................................................36 Figure 5.1 A neuron.......................................................................................................42 Figure 5.2 Four-layer feed-forward neural network classifier........................................43 Figure 5.3 Part of the training data.................................................................................46 Figure 5.4 Convergence of the training performance status line with goal line.............47 Figure 5.5 Part of the test data........................................................................................48 Figure 5.6 Test results....................................................................................................48 Figure 6.1 GUIDE Interface...........................................................................................51 Figure 6.2 Program Interface..........................................................................................52 LIST OF TABLES..................................................................................................................................75

Table 2.1 Skills and methods to complete project..........................................................8 Table 4.2.1 ANOVA test result for Blood Vessels........................................................21 Table 4.3.1 ANOVA test result for Exudates.................................................................28 Table 4.4.1 ANOVA test result for Microaneurysms....................................................34 5 Department of Biomedical Engineering,KLECET,Belgaum

Automatic Exudates Detection From Diabetic Retinopathy, Retinal Image Using Fuzzy C-Means &Morphological structure Table 4.5.1 ANOVA test result for Entropy..................................................................39 Table 4.5.2 ANOVA test result for Homogeneity.........................................................39 Table 4.6.1 Overall images of Features Extraction........................................................40 Table 4.7.1 Overall ANOVA test results.......................................................................41 Table 5.3.1 Representation of different stages...............................................................45 Table 5.3.2 Breakdown of data by Classes....................................................................49 Table 5.3.3 Sensitivity, specificity, predictive accuracy of the Classifier.....................49

6 Department of Biomedical Engineering,KLECET,Belgaum

2012

Automatic Exudates Detection From Diabetic Retinopathy, Retinal Image Using Fuzzy C-Means &Morphological structure

CHAPTER 1. INTRODUCTION This chapter gives a background of diabetic retinopathy and the classification of the images for different stages.

1.1 Background of project Diabetic retinopathy is a complication of diabetes and is a major cause of blindness in developed countries. It is estimated to account for 12% of all the new cases of blindness in United States annually [1]. In Singapore, retinal disease accounts for more than half of the newly registered blindness with diabetic retinopathy as one of the main contributors [2]. It is estimated that about 10% of the population over the age of 40 are affected with diabetes and about 20% of this group will develop some form of diabetic complications in the eye. With the number rising every year, Singapore is one of the countries with the highest rate of diabetes in the world [3].

Figure 1.1 Normal Vision

Figure 1.2 Vision of a person with diabetic retinopathy

As a diabetic tends to have a lot of other health complications, going blind can have his problems multiplied. He will have problems in moving around and even in taking his medications. The patients might not notice a loss of vision until it became too severe, hence early diagnosis and timely treatment is vital to delay or prevent even blindness.

7 Department of Biomedical Engineering,KLECET,Belgaum

visual impair and

2012

Automatic Exudates Detection From Diabetic Retinopathy, Retinal Image Using Fuzzy C-Means &Morphological structure

Currently, regular screenings are conducted and retinal images are obtained fundus camera. However, a large amount of images are obtained from these

using

screenings

and it requires trained ophthalmologists to spend a lot of time for manual analysis and diagnosis [4].

Hence, automatic detection is desired as it can help to

productivity and be more cost effective.

Figure 1.3 Example of a fundus camera

8 Department of Biomedical Engineering,KLECET,Belgaum

improve

2012

Automatic Exudates Detection From Diabetic Retinopathy, Retinal Image Using Fuzzy C-Means &Morphological structure

1.2

Classification of diabetic retinopathy Diabetic retinopathy occurs when the blood vessels of the retina in the

posterior part of the eye are damaged. Damages due to small vessels would be known as microvascular disease while damages due to the arteries would be macrovascular disease.

Figure 1.4 Cross sectional view of the human eye

Generally, diabetic retinopathy is classified into two main stages, namely nonproliferative diabetes retinopathy

(NPDR) and proliferative diabetes retinopathy

(PDR).

1.2.1 Non-proliferative diabetic retinopathy In NPDR, depending on the presence and extent of the features such as hard exudates, microaneurysms or cotton wools spots due to leakage of fluid and blood from the blood vessels, can be classified to mild, moderate or severe stages as followings: 1. Mild NPDR: This is the earliest stage of retinopathy and vision is usually normal except in some cases. However, deterioration of the blood vessels in 9 Department of Biomedical Engineering,KLECET,Belgaum

2012

Automatic Exudates Detection From Diabetic Retinopathy, Retinal Image Using Fuzzy C-Means &Morphological structure

the retina has already started. Blood vessels erupt when there is not enough oxygen in the blood because of high levels of glucose. Small swellings known as Micro-aneurysms or flame-shaped hemorrhages start to develop in the fundus quadrants. 2. Moderate NPDR: As the disease progresses, some of the blood vessels that irrigate the retina become blocked. It is more than

“mild” but less than

“severe” stage. There will be micro-aneurysms or hemorrhages of greater severity in one to three quadrants and leakage might occur, resulting cotton wool spots and exudates etc to be present in the retina. 3. Severe NPDR: As more blood vessels are blocked, those areas in the retina will be deprived of blood supply. Signals will then be sent to the body for the growth of new vessels in order to compensate for the lack of nourishment. The disease would be considered severe NPDR stage when any of the following criteria are met: • Severe (more than 20) hemorrhages and micro-aneurysms in all four quadrants of the fundus • Definite venous beading in at least two quadrants • Severe damage to the small blood vessels in at least one quadrant but no signs of any proliferative diabetic retinopathy.

ical Disk Capillaries

1.2.2 Proliferative diabetic retinopathy PDR is the advanced stage whereby signals are sent by the retina to the body 10 Department of Biomedical Engineering,KLECET,Belgaum

2012

Automatic Exudates Detection From Diabetic Retinopathy, Retinal Image Using Fuzzy C-Means &Morphological structure

for the lack of blood supply and this triggered the growth of new blood vessels. These blood vessels can grow along the retina and the surface of the jelly-like substance (vitreous gel) which fills the centre of the eye. Although they are fragile and abnormal, they do not cause symptoms or vision loss. It is only when their thin and weak walls leak blood, severe visual loss or even irreversible blindness would occur [5].

Normal Mild NPDR Moderate NPDR Severe NPDR PDR Figure 1.6 Retinal fundus images of different stages of diabetic retinopathy [6]

11 Department of Biomedical Engineering,KLECET,Belgaum

2012

Automatic Exudates Detection From Diabetic Retinopathy, Retinal Image Using Fuzzy C-Means &Morphological structure

CHAPTER 2. MATERIALSAND METHOD This chapter gives an overview of the project which outlines the objective and approach of the project.

2.1 Project Object The objective of this project is to develop a computer-based approach to detect the different diabetic retinopathy stages using fundus images. A simple and user friendly interface is also necessary as the user might not be proficient in programming code.

2.2 Overall Object The overall objective of this project is to create an automated program to quickly process a large number of fundus images from mass screening of diabetic retinopathy accurately. Diabetic retinopathy in non-proliferative diabetes retinopathy or proliferative diabetes retinopathy stages can lead to visual impairment or even blindness. Hence, this system could assist ophthalmologists to improve their productivity, efficiency as well as cost effective, in detecting the different stages of diabetic retinopathy and identify patients for early treatment to prevent or delay visual loss.

12 Department of Biomedical Engineering,KLECET,Belgaum

2012

Automatic Exudates Detection From Diabetic Retinopathy, Retinal Image Using Fuzzy C-Means &Morphological structure

2.3 Project Approach This project uses the classification system of Normal, Mild and Higher stages to classify the NPDR and PDR as mentioned in section 1.2. The first approach to this project is a background study of fundus images to determine the features to extract and use. The fundus images are also analyzed to recognize their similarity and differences. Familiarizing with Matlab Image Processing Toolbox is also important prior to the commencing of features extraction. After getting the values of the features from the images, Artificial Neural Network (ANN) would be studied and applied for automatic detection. The results would be tested for accuracy and troubleshooting or fine-tuning of the codes might be required. Lastly, a Graphical User Interface (GUI) to access the automated program would be developed and all the findings and results would be recorded and documented.

13 Department of Biomedical Engineering,KLECET,Belgaum

2012

Automatic Exudates Detection From Diabetic Retinopathy, Retinal Image Using Fuzzy C-Means &Morphological structure

2.4 Skills required The table below shows the skills and methods essential for the completion of this project.

Skills

Sources / Methods Published journals

Getting ideas and information

Related articles from internet References and research papers from libraries eg UniSim Project Supervisor

Assessing and evaluating project progress

Targets setting Comparison of data Project management Understand Matlab software

Implementation of project

Image Processing Toolbox Artificial Neural Network Graphics

User

Interface

Report writing skills

Presentation

Oral presentation skills Organizing and collate data and diagrams for poster designing

Table 2.1 Skills and methods to complete project

2.5 Software Specification The software required is MATLAB program equipped with Image Processing Toolbox and Neural Network Toolbox as well as Microsoft Office for data storage and word processing.

14 Department of Biomedical Engineering,KLECET,Belgaum

2012

CHAPTER 3. MATLAB FUNCTIONS This chapter outlines the Matlab functions

(namely Image analysis and

enhancement and Morphological operations) that are used intensively in this project for image processing. Matlab has many set of tools available for various operations. The main toolbox used for this project is Image Processing Toolbox. It consists of a wide range of image processing functions to analysis, enhance or perform morphological operations on the images.

3.1 Image analysis and enhancement Image analysis or enhancement is used with the intention of simplifying or adjusting the representation of the image for ease of understanding. It is also known as segmentation. Image segmentation is used to locate the objects or boundaries in the image. In edge detection function, the contours of the objects are extracted from the image. Canny method is used for this project as it is better compared to the other similar

Matlab

functions by having two different thresholds to detect the edges [7].

Figure 3.1 Original image

Figure 3.2 Contours of the image after edge detection

Image segmentation is also the process of selecting of pixels that are similar in certain characteristics such as intensity. The selection criterion is referred as the threshold value and the function for example “im2bw” uses this value to convert the image pixels.

Figure 3.3 Image before (Left) and after applying image segmentation (Right)

3.2 Morphological Operations Morphological operations are a set of image processing operations that analyzes the shapes within the image. It applies a structuring element to the image and output the image of the same size. The output value of each pixel is determined by the neighboring pixels with its corresponding pixel of input image. The size and shape of the structuring element affects the number of pixels being added or removed from the object in the image [7]. The most basic morphological operations used are

dilation and erosion .

Erosion removes pixels on the object boundaries in the image by changing it to the background pixel. This shrinks the object and breaks up a single object. Dilation, on the other hand, adds pixels to the object boundaries by changing the background pixel surrounding it. This enlarges the object and multiple objects could merge together as one. Opening or closing is a single function with the combination of dilation and erosion. In opening, the image would undergo erosion followed by dilation. This removes the small object pixels before enlarging the remaining while in closing, the image would undergo dilation followed by erosion. This removes the small background pixels before enlarging the remaining. In this way, the contours of the object smoothen and small object gaps fused. These functions help to handle noise in the image or adjust it to “enclose” a certain desired object [8].

Original Image

Erosion

Dilation

Opening

Closing

Figure 3.4 Results of morphological operations

CHAPTER 4. FEATURES EXTRACTION This chapter outlines how different features of the fundus images namely Blood vessels, Exudates, microaneurysms and texture properties are extracted using image processing techniques. The values obtained are essential as they represent the image and are necessary in order to classify the images accurately.

4.1 Overall Block Diagram The Figure 4.1 below shows the overall block diagram of the whole program. The fundus images are converted to either green component or grayscale for features extraction of texture analysis. The normalized features’ values are then fed into Artificial Neural Network for classification to Normal, Mild or Higher classes.

Figure 4.1 Block Diagram of Overall System

4.2 Blood Vessels Detection

4.2.1 Introduction Blood vessels are extracted in this project for the identification of diabetic retinopathy. The contrast of the fundus image tends to be bright in the centre and diminish at the side, hence preprocessing is essential to minimize this effect and have a more uniform image. After which, the green channel of the image is applied with morphological image processing to remove the optical disk. Image segmentation is then performed to adjust the contrast intensity and small pixels considered to be noise are removed. Another green channel image is processed with image segmentation and combined with the mask layer. These two images are compared and the differences are removed. The obtained image would represent the blood vessels of the original image.

Figure 4.2.1 Block Diagram for Blood Vessels Detection

4.2.2 Experimental procedure - Detection of blood vessels This section discusses in greater detail of the extraction of the blood vessels. The fundus image is first preprocessed to standardize its size to

576x720. The

intensity of the green channel is then inversed before adaptive histogram equalization is applied.

Figure 4.2.2 Original fundus image

Figure 4.2.3 Inversed image after histogram equalization

The optical disk is a black patch in the image as shown at

Figure 4.2.3 .

Morphological opening which consisted of erode followed by dilate is applied. Erode function protects the small blood vessels by reducing their sizes while dilate function blows up the larger remaining details which are intended to be removed. The optical disk is then removed by subtracting Figure 4.2.3 with Figure 4.2.4.

Figure 4.2.4 Image after Morphological opening

Figure 4.2.5 Image after optical disk removed

The image ( Figure 4.2.5 ) is then converted to a binary image using the function “im2bw”. The pixels of the input image are converted to binary 1 (white) for values greater than the selected threshold and to binary 0 (black) if otherwise. The converted binary image

(Figure 4.2.6) at this point is still noisy and function

“bwareaopen” is applied remove the small area of pixels considered to be noise.

Figure 4.2.6 Binary image after Image segmentation

Figure 4.2.7 Blood vessels after removal of “noise”

The green component image (Figure 4.2.2) is also applied with adaptive histogram equalization thrice and image segmentation to select the blood vessels area. Small pixels which are considered as noise are also removed.

Figure 4.2.8 Image after histogram equalization

Figure 4.2.9 Blood vessels after image segmentation and removal of “noise”

Some blood vessels are lost at the optical disk region after applying image segmentation. Hence, a mask is created to retain those blood vessels located when AND logic is applied. The creation of the mask is further discussed at section 4.2.4. The image (Figure 4.2.9) is combined with the mask and compared with the earlier blood vessel image ( Figure 4.2.7 ) using AND logic. The similar pixels are output as binary 1(white) and represent the blood vessels. This is further discussed at section 4.2.5.

Figure 4.2.10 Mask at the optical disk area

Figure 4.2.11 Combined image after removing “noise”

The final blood vessels image (Figure 4.2.12) is obtained after the removal of the circular border. The creation of the border is further discussed the following 4.2.3.

Figure 4.2.12 Image of Blood vessels

section

4.2.3 Experimental procedure - Border Formation There are two methods in detecting the circular border of the image. Both methods are essential as each method could not work for a few of the images due to their intensity contrast. Deploying both methods allows the detection of all the images. Border formation is to clean off the noisy edges and is also used during Exudates and Microaneurysms detection.

4.2.3.1 Border Formation Method 1 Grayscale image instead of the green channel is used as it is more efficient in border detection. The first method uses canny method to detect the edges before enclosing the circular region with a top and bottom bar. Function “imfill” is then applied to fill the region. The circular border is obtained after subtracting the dilated image with the eroded image.

Figure 4.2.13 Block Diagram for Border Formation - Method 1

4.2.3.2 Border Formation Method 2 Method 2 is activated when a noisy image is obtained instead of a circular border. This method inverses the intensity of the image first before image segmentation is applied with the function “im2bw”. The circular region is filled as a result and the circular border is obtained after subtracting the dilated image with the eroded image.

Figure 4.2.14 Block Diagram for Border Formation - Method 2

Figure 4.2.15 Example of Circular border obtained using either method

4.2.4 Experimental procedure - Mask Creation for Optical Disk As the optical disk is made up of a group of bright spots, it is not suitable to use loops and locate the largest value. This would only point to one spot and most likely to be on the side of the optical disk. The mask required to cover the optical disk would be inefficient as it would be much larger and covers more details. Mask creation is used in the detection of blood vessels, exudates and microaneurysms. Matlab Code: max_GB_column = max(Grayscale_brighten_9); %max value for each 720 columns max_GB_single = max(max_GB_column); %max value of the columns [row,column] = find(Grayscale_brighten_9 == max_GB_single); %get the row & column of the image that match the largest value median_row = floor(median(row)); %find 50th percentile and round off median_column = floor(median(column));

Grayscale image instead of the green channel is used as it is more efficient in the detection. The above lines would first find the max value for each of the 720 columns of the image before locating the largest value. The coordinates (row and column) of all brightest point(s) are then determined and the median is taken if there is more than one point. Matlab Code: radius = 90; %size of the mask [x,y]= meshgrid(1:720, 1:576); %row size 576, column size 720 (576x720) mask = sqrt((x-median_column).^2+(y-median_row).^2)