ChongLingChee_FYP2010.docx - CAPSTONE PROJECT

SIM University School of Science and Technology AUTOMATED DETECTION OF DIABETIC RETINOPATHY USING DIGITAL FUNDUS IMAGES Student : E0604276 (PI no.)SUPERVISOR : DR rajendra acharya udyavara Project Code: JAN2010/BME/0016 A project report submitted to SIM University in partial fulfilment of the requirements for the degree of Bachelor of Biomedical Engineering November 2010 TABLE OF CONTENTS Page ABSTRACT 5 ACKNOWLEDGEMENT 6 LISTS OF FIGURES7 LIST OF TABLES10 CHAPTER ONE AIMS AND INTRODUCTION11-12 1.1 Background11 1.2 Objectives12 1.3 Scope12 CHAPTER TWO LITERATURE13-22 2.1 Anatomy structure of the human eye13 2.1.1 The Cornea14 2.1.2 The Aqueous Humor14 2.1.3 The Iris14 2.1.4 The Pupil14 2.1.5 The Lens15 2.1.6 The Vitreous Humor15 2.1.7 The Sclera15 2.1.8 The Optic Disc15 2.1.9 The Retina15 2.1.10 Macula16 2.1.11 Fovea16 2.2 Diabetic Retinopathy (DR) and stages16 2.3Diabetic Retinopathy (DR) features18 2.3.1 Blood Vessels18 2.3.2 Microaneurysms19 2.3.3 Exudates20 2.4Diabetic Retinopathy (DR) examination methods20 2.4.1 Opthalmoscopy (Indirect and Direct)20 2.4.2 Fluorescein Angiography21 2.4.3 Fundus Photography21 2.5Diabetic Retinopathy (DR) treatment21 2.5.1 Scatter Laser treatment21 2.5.2 Vitrectomy22 2.5.3 Focal Laser treatment22 2.5.4 Laser Photocoagulation22 CHAPTER THREE METHODS AND MATERIALS23-54 3.1 System block diagram23 3.2 Image processing techniques24 3.2.1 Image preprocessing24 3.2.2 Structuring Element25 3.2.2.1 Disk shaped Structuring Element (SE)26 3.2.2.2 Ball shaped Structuring Element (SE)26 3.2.2.3 Octagon shaped Structuring Element (SE)27 3.2.3 Morphological image processing27 3.2.3.1 Morphological operations28 3.2.3.2 Dilation and Erosion28 3.2.3.3 Dilation29 3.2.3.4 Erosion29 3.2.3.5 Opening and Closing30 3.2.4 Thresholding31 3.2.5 Edge detection32 3.2.6 Median filtering35 3.3 Feature extraction36 3.3.1 Blood vessels detection36 3.3.2 Microaneurysms detection40 3.3.3 Exudates detection44 3.3.4 Texture analysis47 3.4 Significance test48 3.4.1 Student’s t-test48 3.5 Classification49 3.5.1 Fuzzy51 3.5.2 Gaussian Mixture Model (GMM)54 CHAPTER FOUR RESULTS58-60 4.1 Graphical User Interface (GUI)60 CHAPTER FIVE CONCLUSION AND RECOMMENDATION61 CHAPTER SIX REFLECTIONS62-63 REFERENCES64-66 APPENDIX A: BOX PLOT FOR FEATURES (AREA)67-68 APPENDIX B: BLOOD VESSELS MATLAB CODE69-70 APPENDIX C: MICROANEURYSMS MATLAB CODE71-72 APPENDIX D: EXUDATES MATLAB CODE73-74 APPENDIX E: TEXTURES MATLAB CODE75 APPENDIX F: MEETING LOGS76-79 APPENDIX G: GANTT CHART80-81 ABSTRACT Diabetic retinopathy (DR) is the resultant cause of blindness due to diabetes. The main aim for this project is to develop a system to automate the detection of DR using the fundus images. This is first achieved by using the fundus images to image processed them using morphological processing techniques and texture analysis to extract features such as areas of blood vessels, exudates, microaneurysms and textures. Using the significance test on the features to determine which features have statistically significance of around p ≤ 0.05. The selected features are then input to fuzzy and GMM classifier for automatic classification. After which, the best classifier is then used for the final graphical user interface (GUI) based on percentage of correct data rate of 85.2% and average classification rate of 85.2%. ACKNOWLEGEMENTS I would like to thank my family for their support and encouragement. I would like to thank NUH of Singapore for providing me the fundus images for this project. I would like to thank Unisim for the school facilities. I would like to thank Fabian Pang for his patience and guidance on fuzzy and GMM classification. I would like to thank Jacqueline Tham, Vicky Goh, Mabel Loh, Brenda Ang and Audrey Tan for their moral support and encouragement. Last but most importantly, I would like to thank my project supervisor, Dr Rajendra Acharya Udyavara for his kindness, patience, guidance, advice and enlightenment. LIST OF FIGURES Page Figure 2.1: Anatomy structure of the eye13 Figure 2.1.10: Location of macula, fovea and optic disc16 Figure 2.3.1: Retinal blood vessels19 Figure 2.3.2: Microaneurysms in DR19 Figure 2.3.3: Exudates in DR20 Figure 3.1: System block diagram for the detection and classification of diabetic retinopathy24 Figure 3.2.1a: Original image (left) and its histogram (right)25 Figure 3.2.1b: Image after CLAHE (left) and its histogram (right)25 Figure 3.2.2.1: Disk shaped structuring element26 Figure 3.2.2.2: Ball shaped structuring element (nonflat ellipsoid)27 Figure 3.2.2.3: Octagon shaped structuring element27 Figure 3.2.3.3a: Original image29 Figure 3.2.3.3b: Image after dilation with disk shaped SE29 Figure 3.2.3.4a: Original image30 Figure 3.2.3.4b: Image after erosion with disk shaped SE30 Figure 3.2.3.5a: Opening operation with disk shaped image31 Figure 3.2.3.5b: Closing operation with disk shaped SE image31 Figure 3.2.4a: Original image32 Figure 3.2.4b: Image with too high threshold value32 Figure 3.2.4c: Image with too low threshold value32 Figure 3.2.5a: Original image34 Figure 3.2.5b: Sobel34 Figure 3.2.5c: Prewitt34 Figure 3.2.5d: Roberts34 Figure 3.2.5e: Laplacian of Gaussian (LoG)34 Figure 3.2.5f: Canny35 Figure 3.2.6a: Illustration of a 3 x 3 median filter35 Figure 3.2.6b: Original image (left) and image after median filtering (right)36 Figure 3.3.1a: System block diagram for detecting blood vessels36 Figure 3.3.1b: Normal retinal fundus image37 Figure 3.3.1c: Green component37 Figure 3.3.1d: Inverted green component37 Figure 3.3.1e: Image after CLAHE38 Figure 3.3.1f: Image after opening operation38 Figure 3.3.1g: Image after subtraction38 Figure 3.3.1h: Image after thresholding39 Figure 3.3.1i: Image after median filtering39 Figure 3.3.1j: Final image39 Figure 3.3.1k: Final image (inverted)39 Figure 3.3.2a: System block diagram for detecting microaneurysms40 Figure 3.3.2b: Abnormal retinal fundus image40 Figure 3.3.2c: Red component41 Figure 3.3.2d: Inverted red component41 Figure 3.3.2e: Image after Canny edge detection41 Figure 3.3.2f: Image with boundary41 Figure 3.3.2g: Image after boundary subtraction41 Figure 3.3.2h: Image after filling up the holes or gaps42 Figure 3.3.2i: Image after subtraction42 Figure 3.3.2j: Blood vessels detection42 Figure 3.3.2k: Blood vessels after edge detection42 Figure 3.3.2l: Image after subtraction43 Figure 3.3.2m: Image after filling holes or gaps43 Figure 3.3.2n: Final image43 Figure 3.3.3a: System block diagram for detecting exudates44 Figure 3.3.3b: Abnormal retinal fundus image44 Figure 3.3.3c: Green component45 Figure 3.3.3d: Image after closing operation45 Figure 3.3.3e: Image after column wise neighbourhood operation45 Figure 3.3.3f: Image after thresholding46 Figure 3.3.3g: Image after morphological closing46 Figure 3.3.3h: Image after Canny edge detection46 Figure 3.3.3i: Image after ROI46 Figure 3.3.3j: Image after removing optic disc46 Figure 3.3.3k: Image after removing border46 Figure 3.3.3l: Final image47 Figure 3.5: Block diagram of training and testing data50 Figure 3.5.2: Block diagram of GMM method55 Figure 4: Graphical plot for average percentage 59classification results from two classifiers Figure 4.1: GUI60 LIST OF TABLES Page Table 2.2: Summary of the features of diabetic retinopathy18 Table 3.2.3.2: Rules for dilation and erosion28 Table 3.2.5: Methods and description of various edge detection algorithms33 Table 3.4.1: Student’s t-test results49 Table 3.5.1a: testing1, testing2 and testing3 data output using fuzzy classifier52 Table 3.5.1b: testing1 data output calculation using fuzzy classifier53 Table 3.5.1c: testing2 data output calculation using fuzzy classifier53 Table 3.5.1d: testing3 data output calculation using fuzzy classifier54 Table 3.5.2a: testing1, testing2 and testing3 data output using GMM classifier56 Table 3.5.2b: testing1 data output calculation using GMM classifier56 Table 3.5.2c: testing2 data output calculation using GMM classifier57 Table 3.5.2d: testing3 data output calculation using GMM classifier57 Table 4a: Fuzzy classification results58 Table 4b: GMM classification results59 CHAPTER ONE AIMS AND INTRODUCTION 1.1BACKGROUND Diabetes mellitus or commonly known as diabetes is a chronic systemic disease of disordered metabolism of carbohydrate, protein and fat[21]; most notably known for its condition in which a person has a high blood sugar (glucose) level and as a result of the body either not able to produce enough insulin (type 1 insulin-dependent diabetes mellitus or IDDM[48]) or insulin resistance (type 2 non-insulin-dependent diabetes mellitus or NIDDM[48]). Diabetes is always a disease burden[32], especially in developed countries. According to Ministry Of Health (MOH) in Singapore, 8.2% of total population suffered from diabetes in 2004[32]. Diabetic retinopathy (DR) is one of the complications resulted from prolonged diabetic condition usually after ten to fifteen years of having diabetes. In the case of DR, the high glucose level or hyperglycemia causes damage to the tiny blood vessels inside the retina. This tiny blood vessels will leak blood and fluid on the retina, forming features such as microaneurysms, haemorrhages, hard exudates, cotton wool spots, or venous loops[47]. DR affects about 60% of patients having diabetes for 15 years or more and a percentage of these are at risk of developing blindness[44] in Singapore. Despite these intimidating statistics, research indicates that at least 90% of these new cases could be reduced if there was proper and vigilant treatment and monitoring of the eyes[50]. Laser photocoagulation is an example of surgical method that can reduce the risk of blindness in people who have proliferative retinopathy[9]. However, it is of vital importance for diabetic patients to have regular eye checkups. Current examination methods use to detect and grade retinopathy include ophthalmoscopy (indirect and direct)[23], photography (fundus images) and fluorescein angiography. These methods of detection and assessment of diabetic retinopathy is manual, expensive and require trained ophthalmologists. Therefore, it is important to have an automatic detection method for diabetic retinopathy in an early stage to retard the progression in order to prevent blindness, thus encouraging improvement in diabetic control. It can also reduce the total annual economic cost of diabetes significantly. 1.2OBJECTIVES The objective of this project is to implement an automated detection of diabetic retinopathy (DR) using digital fundus images. By using MATLAB to extract and detect the features such as blood vessels, microaneurysms, exudates and textures which will determine two general classifications: normal or abnormal (DR) eye. An early detection of diabetic retinopathy enables medication or laser therapy to be performed to prevent or delay visual loss. 1.3SCOPE The scope of this project involves using various MATLAB imaging techniques (eg; converting image to binary format, erosion, dilation, boundary detection, etc) to obtain the desire final image and area of the features (eg: blood vessels, microaneurysms, exudates and textures) before using the values to do significance test (eg: student’s t-test) to determine the accuracy of the results obtained mentioned earlier. Next, using the chosen results obtained from student’s t-test to insert into the classifier (eg: Fuzzy and Gaussian Mixture Model or GMM) to obtain the average classification rate, sensitivity and specificity and to classify them into normal and abnormal classes. Lastly, using the data collected to develop a graphical user interface (GUI) for displaying normal or abnormal (DR) eye images based on the best classifier. CHAPTER TWO LITERATURE This section will discuss about the structure of the eye, definition of diabetic retinopathy (DR) and stages, examination and treatment methods and DR features. 2.1Anatomy structure of the human eye The eye is a hollow, spherical organ about 2.5cm in diameter. It has a wall composed of three layers, and its interior spaces are filled with fluids that support the walls and maintain the shape of the eye[45]. Figure 2.1 shows the cross-sectional structure of the eye. The eyes are so important that four-fifth of all of the information the brain receives, come from the eyes. Section 2.1 will explain some of the important parts of the eye. Figure 2.1: Anatomy structure of the eye[3] 
2.1.1The Cornea
The cornea is a transparent medium situated in the front of the eye covering the iris, pupil and anterior chamber that helps to focus incoming light[20] with a water content of 78%[38]. The cornea is elliptical in shape with a vertical and horizontal diameter of 11 and 12mm, respectively[38]. The cornea is supplied with oxygen and nutrients through tear-fluid and not through blood vessels[28]. Therefore, there are no blood vessels in it. The function of the cornea is to refract and transmit light[38]. 
2.1.2The Aqueous Humor
The aqueous humor contains aqueous fluid in the front part of the eye between the lens and the cornea. The aqueous fluid’s main function is to supply the cornea and the lens with nutrients and oxygen[28]. 
2.1.3The Iris
The iris is a thin, pigmented, circular structure in the eye which regulates the amount of light that enters the eye[28]. The function of the iris is to control the size of the pupil by adjusting it to the intensity of the lighting conditions[38]. By expanding the size of the pupil, more light can then enter. This reflex known as the Accommodation Reflex[28] expands the pupil to allow more light to enter when focusing on distant objects or in the darkness. 
2.1.4The Pupil
The pupil is a hole in the center of the iris. The size of the pupil determines the amount of light that enters the eye. The pupil size is controlled by the dilator and sphincter muscles of the iris[42]. It appears black because most of the light entering the pupil is absorbed by the tissues inside the eye[36]. 
2.1.5The Lens
The lens is a transparent, biconvex structure in the eye that, along with the cornea, helps to refract light to be focused on the retina[27]. By changing the shape of the lens, the lens is able to change the focal distance of the eye so it can focus on objects at different distances, thus allowing sharp image to form on the retina. 
2.1.6The Vitreous Humor
The vitreous humor contains clear fluid which fills the eyeball (between the lens and the retina). It is the largest domain of the human eye. The fluid contains more than 95% of water. 
2.1.7The Sclera
The sclera is the white opaque tissue that acts as the eye protective outer coat. Six tiny muscles connect to it around the eye and control the eye's movements. The optic nerve is attached to the sclera at the very back of the eye[42]. 
2.1.8The Optic Disc
The optic disc, also known as the optic nerve head or the blind spot, the optic disc is where the optic nerve attaches to the eye[28]. There are no light sensitive rods or cones to respond to a light stimulus at this point. This causes a break in the visual field called "the blind spot" or the "physiological blind spot"[35]. Figure 2.1.10 shows the location of the optic disc. 
2.1.9The Retina
The retina is a thin layer of neural cells[38] that lines in the inner back of the eye. It is light sensitive and absorbs light. The image signals are received and send to the brain. The retina contains two kinds of light receptors; rods and cones. The rods absorb light in black and white. The rods are responsible for night vision. The cones are colour sensitive and absorb stronger light. The cones are responsible for colour vision. 
2.1.10Macula
The macula is the area around the fovea[28]. It is an oval-shaped highly pigmented yellow spot near the center of the retina[31] as shown in Figure 2.1.10. It is a small and highly sensitive part of the retina responsible for detailed central vision. MaculaFoveaOptic discFigure 2.1.10: Location of macula, fovea and optic disc 
Fovea
The fovea is the most central part of the macula. The visual cells located in the fovea are packed tightest, resulting in optimal sharpness of vision. Unlike the retina, it has no blood vessels to interfere the passage of light striking the foveal cone mosaic[15]. Figure 2.1.10 shows the location of fovea. 
Diabetic Retinopathy (DR) and stages
Diabetes is the chronic state caused by an abnormal increase in the glucose level in the blood and which causes the damage to the blood vessels. The tiny blood vessels that nourish the retina are damaged by the increased glucose level[47]. Diabetic retinopathy (DR) is one of the complications that affect retinal capillaries. This effect causes thickening of arterial wall and blockage of blood flow to the eye occurs. DR can be broadly classified as non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR)[47] as shown in Figure 2.2. There are four DR stages: Stage 1 – Background diabetic retinopathy (also termed mild or moderate non-proliferative retinopathy). At least one microaneurysm with or without the presence of retinal haemorrhages, hard exudates, cotton wool spots or venous loops will be present[6,7]. Stage 2 – Moderate non-proliferative retinopathy. Numerous microaneurysms and retinal haemorrhages will be present. Cotton wool spots and a limited amount of venous beading can also be seen[47]. Some blood vessels are starting to become blocked. Stage 3 – Severe non-proliferative retinopathy. Many features such as haemorrhages and microaneurysms are present in the retina. Other features are also present except less growth of new blood vessels; many more blood vessels are now blocked and these areas of the retina start to send signals to the body to grow new blood vessels for nourishment[38]. Stage 4 – Proliferative retinopathy. PDR is the advanced stage where the fluids sent by the retina for nourishment trigger the growth of new blood vessels[22]. The main blood vessels become stiff and blockage of blood flow occurs. Small pockets of blood begin to form around the boundary of the main blood vessels. These fragile blood vessels have thin walls and when the walls burst, blood spatters form. Exudates (proteins and other lipids) and blood from the leakage forms around the retina and in some cases, leakage may form on the fovea, resulting in sudden severe vision loss and blindness. Figure 2.2: Stages of DR fundus images[51] The features of each stage are summarised in Table 2.2. ClassificationAlternative terminologyFeaturesBackground diabetic retinopathyMild/moderate non-proliferative diabetic retinopathyHaemorrhagesOedemaMicroaneurysmsExudatesCotton wool spotsDilated viensPre-proliferative diabetic retinopathySevere/very severe non-proliferative retinopathyDeep retinal haemorrhages in four quadrantsVenous abnormalitiesIntraretinal microvascular abnormalities (IRMA)Multiple cotton wool spotsProliferative diabetic retinopathyProliferative diabetic retinopathy (PDR)New vessels on optic discNew vessels elsewhereAdvanced diabetic eye diseaseComplications of proliferative diabetic retinopathyVitreous haemorrhageRetinal detachmentNeovascular glaucoma Table 2.2: Summary of the features of diabetic retinopathy[18] 
Diabetic Retinopathy (DR) features
There are many features which are present in a DR eye. However, since the main objective of this project is to have an automated system for early DR detection on some of the extracted features. Therefore, features such as blood vessels, microaneurysms, exudates and textures (in feature extraction section) will be discussed. 
Blood Vessels
In normal retina, the main function of the blood vessels is to send nutrients such as oxygen and blood to the eye (Figure 2.3.1). In the case of DR, the simulation to the growth of new fragile blood vessels is due to the blockage and thickening of the main blood vessels. When the main blood vessels are blocked, new vessels are triggered to grow in an attempt to send oxygen and nourishment to the eye. However, these new blood vessels are very fragile and abnormal. They are prone to rupture and leak fluids (proteins and lipids) and blood into the eye. This may not hinder the patient’s sight if the leakage does not occur on the fovea or macula. However, if the blood spatters happen to be on the fovea or macula, sudden loss of vision in that eye occurs as the spatters block all light entering into the eye.
RetinalBloodVessels Figure 2.3.1: Retinal blood vessels 
Microaneurysms
Microaneurysms are small saclike out pouching in the small vessels and capillaries[25] as shown in Figure 2.3.2. They are an early feature of DR and it appears as small red dots due to the ballooning of capillaries. They represent a small weakness in the retinal capillary wall that leaks blood and serum[18]. They appear as tiny red dots in fundus photographs.
MicroaneurysmsFigure 2.3.2: Microaneurysms in DR 
Exudates
Exudates often described as hard exudates, these are deposits of extravasated plasma proteins, especially lipoproteins as shown in Figure 2.3.3. They leak into retinal tissue with serum, and are left behind as oedema fluid is absorbed. Eventually exudates are cleared from the retina by macrophages[18]. They appear as yellow-white dots within the retina. The yellow deposits may be seen as either individual spots or clusters[25], usually near optic disc.
Sometimes the exudates may be formed on macula or fovea, as a result, there will be sudden loss of vision in that eye, regardless of the diabetic retinopathy stages.
ExudatesFigure 2.3.3: Exudates in DR
Diabetic Retinopathy (DR) examination methods There are few types of DR examination methods but mainly ophthalmoscopy (indirect and direct), fluorescein angiogram and fundus photography. 
Opthalmoscopy (Indirect and Direct)
Direct opthalmoscopy is the examination method performs by the specialist in a dark room. A beam of light is shined through the pupil using opthalmoscope. This allows the specialist to view the back of the eyeball.
Indirect opthalmoscopy is performed with a head or spectacles-mounted source of illumination positioned in the middle of the forehead[26]. A bright light is shined into the eye using the instrument on the forehead. The condensing lens is placed on the eye to intercept the fundus reflex. A real and inverted image of the fundus will form between the examiner and the patient[26].
Fluorescein Angiography
Fluorescein angiography is a test which allows the blood vessels in the back of the eye to be photographed as a fluorescent dye is injected into the bloodstream via the hand or arm[49]. The pupils will be dilated with eye drops and the yellow dye (Fluorescein Sodium) is injected into a vein in the arm[49]. It is used to examine the blood circulation of the retina using the dye tracing method.
Fundus Photography
Fundus photography is the usage of fundus camera to photograph the regions of the vitreous, retina, choroid and optic nerve[16]. Fundus photographs are only considered medically necessary where the results may influence the management of the patient. In general, fundus photography is performed to evaluate abnormalities in the fundus, follow the progress of a disease, plan the treatment for a disease, and assess the therapeutic effect of recent surgery[16]. In this report, the images for imaging processing were taken from fundus camera. Diabetic Retinopathy (DR) treatment Treatment of diabetic retinopathy varies depending on the extent of the disease[10]. During the early stages of DR, no treatment is needed unless macular oedema is present. However, for advanced DR such as proliferative diabetic retinopathy, surgery is necessary. 
Scatter Laser treatment
Advanced stage diabetic retinopathy is treated by performing scatter laser treatment. During scatter laser treatment, an ophthalmologist uses a laser to "scatter" many small burns across the retina. This causes leaking and abnormal blood vessels to shrink[10]. This surgical method is used to reduce vision loss. However, if there is significant amount of haemorrhages, scatter laser treatment is not suitable.
Vitrectomy
A vitrectomy is performed under either local or general anesthesia. An ophthalmologist makes a tiny incision in the eye and carefully removes the vitreous gel that is clouded with blood. After the vitreous gel is removed from the eye, a clear salt solution is injected to replace the contents[10].
Focal Laser treatment
Leakage of fluid from blood vessels can sometimes lead to macular oedema, or swelling of the retina. Focal laser treatment is performed to treat macular oedema. Several hundred small burns are placed around the macula in order to reduce the amount of fluid build-up in the macula[10].
Laser Photocoagulation
Laser photocoagulation is a powerful beam of light which, combined with ophthalmic equipment and lenses, can be focused on the retina[41]. Small bursts of laser are used to seal leaky blood vessels, destroy abnormal blood vessels, seal retinal tears, and destroy abnormal tissue in the back of the eye[41]. This procedure is used to treat diabetic retinopathy patients in proliferative diabetic retinopathy stage. The main advantage of using this surgical method is the short surgical duration and the patient usually can resume activities immediately.
CHAPTER THREE METHODS AND MATERIALS Total 60 fundus images from various demographics are used in this project. These fundus images were taken from the ophthalmology department in National University Hospital (NUH) of Singapore. The images were taken in 720 x 576 pixels. System block diagram Figure 3.1 shows the system block diagram for identification of diabetic retinopathy. Using the input image, the image is processed using the image processing techniques using MATLAB. Features such as, areas of blood vessels, microaneurysms, exudates and textures are extracted. The extracted features are then inserted into Student’s t-test to generate significance test (probability of true significance). Using the Student’s t-test results (results which have high probability of true significance) to the classifiers (eg: Fuzzy and Gaussian Mixture Model or GMM), the average classification rate, sensitivity and specificity, etc are generated. Lastly, at the final stage, using the results generated from the classifiers to determine the diabetic retinopathy (DR) classes; normal and abnormal. 
Input ImageImageProcessingTechniquesFeature ExtractionAreas of Blood vesselsMicroaneurysmsExudatesTexturesClassificationFuzzy and GMM ClassifiersSignificance TestStudent’s t-testNormalAbnormal
Figure 3.1: System block diagram for the detection and classification of diabetic retinopathy
Image processing techniques The image processing techniques are used to enhance the images, morphological image processing and texture analysis. They are also used to reduce image noise, contrast and invert the images. 3.2.1image preprocessing Before image processing is carried out, the fundus images need to be preprocessed to remove non-uniform background. Non-uniform brightness and variation in the fundus images are the main reasons for non-uniformity. Therefore, the error needs to be corrected by applying contrast-limited adaptive histogram equalization (CLAHE) to the image before applying the image processing operations[22]. A histogram is a graph which indicates the number of times each gray level occurs in an image. For example, in bright images, the gray levels will be clustered at the upper end of the graph. As for images that are darker, the gray levels will then be at the lower end of the graph. For a gray level that is evenly spread out in the histogram, the image is well-contrasted. CLAHE operates on small regions in the image, called tiles. Each tile's contrast is enhanced, so that the histogram of the output region approximately matches a specified histogram[2]. Figure 3.2.1a shows the fundus image before CLAHE and its histogram shows more bright level regions than dark level regions. Figure 3.2.1b shows the fundus image after CLAHE and its histogram shows an evenly distributed brightness. Figure 3.2.1a: Original image (left) and its histogram (right) Figure 3.2.1b: Image after CLAHE (left) and its histogram (right) 3.2.2structuring element A structuring element (SE) is a binary morphology that is used to probe the image. It is a matrix consisting of only 0's and 1's that can have any arbitrary shape and size. The pixels with values of 1 define the neighbourhood[34]. There are two types of SE, two-dimensional or flat SE usually consists of origin, radius and approximation N value. Three-dimensional or nonflat SE usually consists of radius (x-y planes), height and approximation N value. There are different types of SE shapes but in this project, disk shaped, ball shaped and octagon shaped SE are used. 3.2.2.1Disk SHAPED STRUCTURING element (se) Disk shaped SE, SE = strel('disk', R, N) creates a flat, disk shaped structuring element, where R specifies the radius. R must be a nonnegative integer. N must be 0, 4, 6, or 8[8]. Figure 3.2.2.1 shows a disk shaped SE with radius 3 and its centre of origin. Figure 3.2.2.1: Disk shaped structuring element 3.2.2.2BALL SHAPED STRUCTURING element (se) Ball shaped SE, SE = strel('ball', R, H, N) creates a nonflat, ball-shaped structuring element (actually an ellipsoid) whose radius in the X-Y plane is R and whose height is H. Note that R must be a nonnegative integer, H must be a real scalar, and N must be an even nonnegative integer[8]. Figure 3.2.2.2 shows a ball shaped SE with x-y axis as radius and z axis as height. Figure 3.2.2.2: Ball shaped structuring element (nonflat ellipsoid) 3.2.2.3octagon SHAPED STRUCTURING element (se) Octagon shaped SE, SE = strel('octagon', R) creates a flat, octagonal structuring element, where R specifies the distance from the structuring element origin to the sides of the octagon, as measured along the horizontal and vertical axes. R must be a nonnegative multiple of 3[8]. Figure 3.2.2.3 shows an octagon shaped SE with radius 3 and its centre of origin. Figure 3.2.2.3: Octagon shaped structuring element 3.2.3Morphological image processing Morphological image processing is a branch of image processing that is particularly useful for analyzing shapes in images[3]. Mathematical morphology is the foundation of morphological image processing, which consists of a set of operators that transform images according to size, shape, connectivity, etc. 3.2.3.1morphological operations Morphological operations are used to understand the structure or form of an image. This usually means identifying objects or boundaries within an image. Morphological operations play a key role in applications such as machine vision and automatic object detection[33]. Morphological operations apply a structuring element to an input image, creating an output image of the same size. In a morphological operation, the value of each pixel in the output image is based on a comparison of the corresponding pixel in the input image with its neighbours. By choosing the size and shape of the neighborhood, a morphological operation can be created that is sensitive to specific shapes in the input image[34]. There are many types of morphological operations such as dilation, erosion, opening and closing. 3.2.3.2Dilation and erosion Dilation and erosion are basic morphological processing operations. They are defined in terms of more elementary set operations, but are employed as the basic elements of many algorithms. Both dilation and erosion are produced by the interaction of structuring element with a set of pixels of interest in the image[19]. Dilation adds pixels to the boundaries of objects in an image, while erosion removes pixels on object boundaries. The number of pixels added or removed from the objects in an image depends on the size and shape of the structuring element used to process the image. In the morphological dilation and erosion operations, the state of any given pixel in the output image is determined by applying a rule to the corresponding pixel and its neighbours in the input image. The rule used to process the pixels defines the operation as a dilation or an erosion[34]. Table 3.2.3.2 shows the operations and the rules. OperationRuleDilationThe value of the output pixel is the maximum value of all the pixels in the input pixel's neighborhood. In a binary image, if any of the pixels is set to the value 1, the output pixel is set to 1.ErosionThe value of the output pixel is the minimum value of all the pixels in the input pixel's neighborhood. In a binary image, if any of the pixels is set to 0, the output pixel is set to 0. Table 3.2.3.2: Rules for dilation and erosion[34] 3.2.3.3Dilation Suppose A and B are sets of pixels. Then the dilation of A by B, denoted A⊕B, is defined as A⊕B= ∪x∈BAx. This means that for every point x∈B, A is translated by those coordinates. An equivalent definition is that A⊕B={x,y+u,v:(x,y)∈A,(u,v)∈B}. Dilation is seen to be commutative, that A⊕B= B⊕A[3]. Figure 3.2.3.3a shows an original fundus image before dilation and Figure 3.2.3.3b shows the same image after dilation with disk shaped SE of radius 8. Optic disc becomes more prominent and exudates can also be seen near macula. Figure 3.2.3.3b: Image after dilation with disk shaped SE Figure 3.2.3.3a: Original image 3.2.3.4erosion 
Given sets A and B, the erosion of A by B, written A⊝B, is defined as A⊝B={w:Bw⊆A}[3]. Figure 3.2.3.4a shows an original fundus image before dilation and Figure 3.2.3.4b shows the same image after erosion with disk shaped SE of radius 8. Blood vessels become more prominent.
Figure 3.2.3.4b: Image after erosion with disk shaped SE Figure 3.2.3.4a: Original image 
3.2.3.5opening and closing
Dilation and erosion are often used in combination to implement image processing operations[34]. Erosion followed by dilation is called an open operation. Opening of an image smoothes the contour of an object, breaks narrow isthmuses (“bridges”) and eliminates thin protrusions[12]. Dilation followed by erosion is called a close operation. Closing of an image smoothes section of contours, fuses narrow breaks and long thin gulfs, eliminates small holes in contours and fills gaps in contours[12].
Opening operation of image is defined as A∘B=(A⊖B)⊕B[3]. Since opening operation of image consists of erosion followed by dilation, therefore it can also be defined as A∘B=∪{Bw:Bw⊆A}[3]. Closing operation of image is defined as A∙B=(A⊕B)⊖B[3]. Figure 3.2.3.5a and Figure 3.2.3.5b shows the difference between opening operation and closing operation of fundus images. Figure 3.2.3.5a: Opening operation with disk shaped imageFigure 3.2.3.5b: Closing operation with disk shaped SE image 3.2.4thresholding Thresholding turns a colour or grayscale image into a 1-bit binary image. This is done by allocating every pixel in the image either black or white, depending on their value. The pivotal value that is used to decide whether any given pixel is to be black or white is the threshold[17]. Thresholding is useful to remove unnecessary detail from an image to concentrate on essentials[3]. In the case of the fundus image, by removing all gray level information, the blood vessels are reduced to binary pixels. It is necessary to distinguish blood vessels foreground from the background information. Thresholding can also be used to bring out hidden detail. It is very useful in the image region which is obscured by similar gray levels. Therefore, choosing an appropriate threshold value is important because a low value may decrease the size of some of the objects or reduce the number and a high value may include extra background information. Figure 3.2.4a shows original fundus image before thresholding with CLAHE. Figure 3.2.4b shows the same image with too high threshold value resulting in too much background information. Figure 3.2.4c shows the same image with too low threshold value resulting in missing foreground information. Figure 3.2.4a: Original image Figure 3.2.4b: Image with too high threshold value Figure 3.2.4c: Image with too low threshold value 3.2.5Edge detection In an image, an edge is a curve that follows a path of rapid change in image intensity. Edges are often associated with the boundaries of objects in a scene[4]. Edge detection refers to the process of identifying and locating sharp discontinuities in an image[39]. It is possible to use edges to measure the size of objects in an image, isolate particular objects from their background, and to recognize or classify objects[3]. There are generally six edge detection algorithms and they are, Sobel, Prewitt, Roberts, Laplacian of Gaussian (LoG), zero-cross and Canny. Table 3.2.5 shows the six edge detection methods and their descriptions. MethodsDescriptionsSobelThe Sobel method finds edges using the Sobel approximation to the derivative. It returns edges at those points where the gradient of I is maximum.PrewittThe Prewitt method finds edges using the Prewitt approximation to the derivative. It returns edges at those points where the gradient of I is maximum.RobertsThe Roberts method finds edges using the Roberts approximation to the derivative. It returns edges at those points where the gradient of I is maximum.Laplacian of Gaussian (LoG)The Laplacian of Gaussian method finds edges by looking for zero crossings after filtering I with a Laplacian of Gaussian filter.zero-crossThe zero-cross method finds edges by looking for zero crossings after filtering I with a filter the user specify.CannyThe Canny method finds edges by looking for local maxima of the gradient of I. The gradient is calculated using the derivative of a Gaussian filter. The method uses two thresholds, to detect strong and weak edges, and includes the weak edges in the output only if they are connected to strong edges. This method is therefore less likely than the others to be fooled by noise, and more likely to detect true weak edges. Table 3.2.5: Methods and description of various edge detection algorithms[14] After comparing all six edge detection algorithms, the Canny method performs better than the others due to the fact that it uses two thresholds to detect strong and weak edges and for this reason, Canny algorithm is chosen for edge detection over the others for this project. Figure 3.2.5a, b, c, d, e, f shows original image, Sobel edge detection, Prewitt edge detection, Roberts edge detection, Laplacian of Gaussian (LoG) edge detection and Canny edge detection methods respectively. It is apparent that by using Canny edge detection method, the weak fine blood vessels can be detected. Figure 3.2.5a: Original image Figure 3.2.5b: Sobel Figure 3.2.5c: Prewitt Figure 3.2.5d: Roberts Figure 3.2.5e: Laplacian of Gaussian (LoG) Figure 3.2.5f: Canny 3.2.6Median filtering Median filtering is a nonlinear operation often used in image processing to reduce "salt and pepper" noise. A median filter is more effective than convolution when the goal is to simultaneously reduce noise and preserve edges[1]. Median of a set is the middle value when values are sorted. For even number of values, the median is the mean of the middle of two[3]. Figure 3.2.6a shows an illustration of a 3 x 3 median filter for a set of sorted values to obtain the median value. 55706555 57 62 63 65 66 68 70 260576826063666562 Figure 3.2.6a: Illustration of a 3 x 3 median filter This method of obtaining the median value means that very large or very small values (noisy values) will be replaced by the value closer to its surroundings. Figure 3.2.6b shows the difference before and after applying median filtering. The “salt and pepper” noise in the original image have been clearly reduced after applying the median filtering. Figure 3.2.6b: Original image (left) and image after median filtering (right) 3.3Feature extraction Features namely, blood vessels, microaneurysms, exudates and textures are extracted. The steps are explained below. 3.3.1Blood vessels detection Figure 3.3.1a shows the system block diagram of blood vessels detection. The detailed steps are explained below. Figure 3.3.1a: System block diagram for detecting blood vessels All coloured images consist of RGB (red, green blue) primary colours channels. Each pixel has a particular colour by the amount of red, green and blue. If each colour component has a range of 0-255 then three components give 2553 = more than 16 million colours. Each pixel consists of 24 bits and therefore is a 24-bit colour image. The fundus images used in this project are 24-bit, 720 x 576 pixels. Normal images as shown in Figure 3.3.1b basically consist of blood vessels, optic disc and macula without any other abnormal features. Blood vessels detection is important in identification of diabetic retinopathy (DR) through image processing techniques. MaculaFoveaOptic discRetinalBloodVessels Figure 3.3.1b: Normal retinal fundus image Firstly, as part of the image preprocessing step, the green component of the image is extracted as shown in Figure 3.3.1c and the green component’s intensity is inverted as shown in Figure 3.3.1d. Figure 3.3.1c: Green component Figure 3.3.1d: Inverted green component After inverting the green component’s intensity, edge detection is performed using Canny method. The border is then detected and a disk shaped structuring element (SE) of radius 8 is created with morphological opening operation (erosion then dilation). Next, subtract the eroded image with the original image and the border or boundary is obtained. Afterwards, adaptive histogram equalization is performed to improve the contrast of the image and to correct uneven illumination as shown in Figure 3.3.1e. A morphological opening operation (erosion then dilation) is performed using the ball shaped structuring element (SE) to smooth the background and to highlight the blood vessels as shown in Figure 3.3.1f. Figure 3.3.1e: Image after CLAHE Figure 3.3.1f: Image after opening operation The image is then subtracted from the adaptive histogram equalized image (CLAHE). As shown in Figure 3.3.1g, the resulting image shows higher intensity at the foreground (blood vessels) as compared with the background – a contrast. Figure 3.3.1g: Image after subtraction From the subtracted image, the image is converted from grayscale to binary by performing thresholding with value of 0.1 as shown in Figure 3.3.1h. Median filtering is performed to remove "salt and pepper" noise as shown in Figure 3.3.1i. The boundary is obtained after subtracting the border with disk shaped SE with image with median filtering. Figure 3.3.1h: Image after thresholding Figure 3.3.1i: Image after median filtering The border is then eliminated after filling the holes that do not touch the edge to obtain the final image as shown in Figure 3.3.1j. The pixel values of the image are inverted to get only the blood vessels with black background as shown in Figure 3.3.1k. The detailed MATLAB code is attached in Appendix B. Figure 3.3.1j: Final image Figure 3.3.1k: Final image (inverted) 3.3.2microaneurysms detection Figure 3.3.2a shows the system block diagram of microaneurysms detection. The detailed steps are explained below. Figure 3.3.2a: System block diagram for detecting microaneurysms Microaneurysms appear as tiny red dots on retinal fundus image as shown in Figure 3.3.2b, therefore the red component of the RGB image are used to identify the microaneurysms as shown in Figure 3.3.2c. Next, the intensity is then inverted as shown in Figure 3.3.2d. Similar to blood vessels detection, Canny method is used for edge detection for microaneurysms detection as shown in Figure 3.3.2e. Microaneurysms Figure 3.3.2b: Abnormal retinal fundus image Figure 3.3.2c: Red component Figure 3.3.2d: Inverted red component The boundary is detected by filling up the holes and a disk shaped structuring element (SE) of radius 8 is created with morphological opening operation (erosion then dilation) as shown in Figure 3.3.2f. The edge detected image is then subtracted from the image with boundary to obtain image without boundary as shown in Figure 3.3.2g. Figure 3.3.2e: Image after Canny edge detection Figure 3.3.2f: Image with boundary Figure 3.3.2g: Image after boundary subtraction After which, the holes or gaps are filled, resulting in microaneurysms and other unwanted artifacts present as shown in Figure 3.3.2h. The image with filled holes or gaps then subtracts the image before filled holes or gaps. The resulting image thus has microaneurysms and other unwanted artifacts without the edge as shown in Figure 3.3.2i. Figure 3.3.2h: Image after filling up the holes or gaps Figure 3.3.2i: Image after subtraction The blood vessels are detected using the same method mentioned in section 3.3.1. Figure 3.3.2j shows the blood vessels detected image. Edge detection Canny method is then used on the blood vessels image to detect the edges as shown in Figure 3.2.2k. This image is then subtracted from the image after boundary subtraction (Figure 3.3.2g). The resulted image is shown in Figure 3.3.2l. Figure 3.3.2k: Blood vessels after edge detection Figure 3.3.2j: Blood vessels detection Figure 3.3.2l: Image after subtraction Finally, after filling the holes or gaps as shown in Figure 3.3.2m, this image is subtracted with the image with microaneurysms and unwanted artifacts to obtain the final image with only microaneurysms as shown in Figure 3.3.2n. The detailed MATLAB code is attached in Appendix C. Figure 3.3.2m: Image after filling holes or gaps Figure 3.3.2n: Final image 3.3.3Exudates detection Figure 3.3.3a shows the system block diagram of exudates detection. The detailed steps are explained below. Figure 3.3.3a: System block diagram for detecting exudates Exudates appear as yellowish dots in the fundus images as shown in Figure 3.3.3b. It is easier to spot them than microaneurysms. In order to detect exudates, firstly similar to blood vessels detection, green component of the RGB image is extracted as shown in Figure 3.3.3c and octagon shaped structuring element (SE) of size 9 is created. A morphological closing is performed on the SE as shown in Figure 3.3.3d. As clearly shown, the exudates become more prominent than the background although the optic disc is also present, as their grey levels are similar. Exudates Figure 3.3.3b: Abnormal retinal fundus image Figure 3.3.3c: Green component Figure 3.3.3d: Image after closing operation Column wise neighbourhood operation is performed to rearrange the image into columns first. The parameter sliding indicates that overlapping neighbourhoods are being used[3]. This operation is performed to remove most of the unwanted artifacts leaving only the border, exudates and the optic disc as shown in Figure 3.3.3e. Figure 3.3.3e: Image after column wise neighbourhood operation Next, thresholding is performed to the image with the threshold value of 0.7 as shown in Figure 3.3.3f. Morphological closing with disk shaped structuring element (SE) of size 10 is used to fill up the holes or gaps of the exudates as shown in Figure 3.3.3g. Figure 3.3.3g: Image after morphological closingFigure 3.3.3f: Image after thresholding The optic disc contains the highest pixel value in the image. Therefore, to remove the optic disc, edge detection using Canny method (Figure 3.3.3h) is used together with region of interest (ROI). First, a radius of 82 is defined as most optic disc is of size 80 x 80 pixels as shown in Figure 3.3.3i. Next, the optic disc is removed together with the border as shown in Figure 3.3.3j and Figure 3.3.3k. Figure 3.3.3i: Image after ROI Figure 3.3.3h: Image after Canny edge detection Figure 3.3.3k: Image after removing borderFigure 3.3.3j: Image after removing optic disc Finally, by performing morphological erosion operation with disk shaped structuring element (SE) of size 3 to obtain the final image with only exudates as shown in Figure 3.3.3l. The detailed MATLAB code is attached in Appendix D. Figure 3.3.3l: Final image 3.3.4texture analysis Texture describes the physical structure characteristic of a material such as smoothness and coarseness. It is a spatial concept indicating what, apart from color and the level of gray, characterizes the visual homogeneity of a given zone of an image[24]. Texture analysis of an image is the study of mutual relationship among intensity values of neighbouring pixels repeated over an area larger than the size of the relationship[22]. The main types of texture analysis are structural, statistical and spectral. Mean, standard deviation, third moment and entropy are statistical type. Mean, standard deviation and third moment are concern with properties of individual pixels. Mean is defined as: Mean = µ1 = [6] and standard deviation is defined as: SD = σ1 = [6]. Third moment is a measure of the skewness of the histogram and is defined as: μ3z=i=0L-1(zi-m)3p(zi)[37]. Entropy is a statistical type of texture that measures randomness in an image texture. An image that is perfectly flat will have entropy of zero. Consequently, they can be compressed to a relatively small size. On the other hand, high entropy images such as an image of heavily cratered areas on the moon have a great deal of contrast from one pixel to the next and consequently cannot be compressed as much as low entropy images[7]. Entropy is defined as: -P log2P. The texture features used in this project are mean, standard deviation, third moment and entropy. The detailed MATLAB code is attached in Appendix E. 3.4Significance test Significance test is to calculate statistically whether set(s) of data is occurred by chance or true occurrence or the level of true occurrence. The significance level is defined as p-value. The lower the p-value, the more statistically significant a set of data is. For example, given set A has a p-value of 0.1 and set B has a p-value of 0.05, then set B is said to be more statistically significant than set A as there is only 5% chance that it could occur by chance or coincidence. Unlike set B, set A has 5% more chance than set B that it could occur by chance or coincidence. The typical level of significance is 5% or p-value ≤ 0.05. Significance test is done prior to classification. 3.4.1Student’s t-test Student’s t-test deals with the problems associated with inference based on “small” samples[46]. When independent samples are available from each population the procedure is often known as the independent samples t-test and the test statistic is: t=x1-x2s1n1+1n2 where x1 and x2 are the means of samples of size n1 and n2 taken from each population[5]. Using the area of the features for blood vessels, microaneurysms, exudates, mean, standard deviation, third moment and entropy into Student’s t-test, the significance test results are generated. Appendix A shows the box plot for various features (area) with high, median and low values. Table 3.4.1 shows the p-values of each set of features (area). The highlighted (yellow) rows indicate that data is statistically significant. Therefore, only the statistically significant sets of data are used in the classification (ie: blood vessels, microaneurysms, mean and third momentes of blood vessels, microaneurysms, exudates, ). After selecting the features, normalization of the data is then processed prior to classification. Normalization is done by dividing each value in the particular feature by the highest value of that particular feature. This is to ensure each value is ≤ 1 > 0 to improve the classification as it will have less distribution among the data. FeaturesMean ± Standard DeviationP-ValueNormalAbnormalBlood Vessels31170 ± 798935950 ± 104300.051Exudates1909 ± 12241477 ± 9570.13Microaneurysms330±238884±564<0.0001TexturesMean74.1± 17.083.3±21.40.072Textures Standard Deviation37.0±6.4239.5±8.360.20TexturesThird Moment0.139±0.609-0.400±0.3890.0001TexturesEntropy4.04±0.3204.13±0.3050.30 Table 3.4.1: Student’s t-test results 3.5Classification For this project, Fuzzy and Gaussian Mixture Model (GMM) are used for automatic classification of diabetic retinopathy (DR). There are 42 training data and 18 testing data. Figure 3.5 shows the block diagram of training and testing data processing prior to inputting to the classifier. Normalized data is first split into 70% and 30%. Step I consists of 70% of normal and abnormal data and 30% of normal and abnormal data. They are then grouped into step III. Train1 and test1 is then further split into set A, B, C and D (step IV). They are then mixed and split into train2, test2 and train3, test3 (step V). Lastly, the training and testing data is exported to MATLAB as variables to load into the classifier. Figure 3.5: Block diagram of training and testing data 3.5.1FUZZY A fuzzy classifier is any classifier which uses fuzzy sets either during its training or during its operation[29]. Fuzzy pattern recognition is sometimes identified with fuzzy clustering or with fuzzy if-then systems used as classifiers[29]. In a fuzzy classification system, a case or an object can be classified by applying a set of fuzzy rules based on the linguistic values of its attributes. Every rule has a weight, which is a number between 0 and 1, and this is applied to the number given by the antecedent. It involves 2 distinct parts. The first part involves evaluating the antecedent, fuzzifying the input and applying any necessary fuzzy operators[40] such as, union: μ A∩B x= Min[μAx, μBx], intersection: μ A∩B x= Min[μAx, μBx], complement: μA-x=1-μA(x) where μ is the membership function[40]. The second part requires application of that result to the consequent, known as inference. A fuzzy inference system is a rule-based system that uses fuzzy logic, rather than Boolean logic, to reason about data[40]. Fuzzy Logic (FL) is a multivalued logic, that allows intermediate values to be defined between conventional evaluations like true/false, yes/no, high/low, etc[30]. These fuzzy rules define the connection between input and output fuzzy variables[40]. Table 3.5.1a shows the output of 3 set of testing data from fuzzy classifier. The correct (true) Boolean rule from nos 1-9 is supposed to be [0, 1] (true positives for normal data) so [1, 0] (false positives) is incorrect (false). Therefore, there are some errors. Likewise for data from nos 10-18, the correct (true) Boolean rule is supposed to be [1, 0] (true negatives for abnormal data) so [0, 1] (false negatives) is incorrect (false). Therefore, there are some errors too. Label 1 denotes normal data and label 2 denotes abnormal data. The correct labeling should be 1 from nos 1-9 and 2 from nos 10-18. Table 3.5.1b-d shows fuzzy testing data for positive predictive value, negative predictive value, sensitivity and specificity calculation. TP denotes true positives, TN denotes true negatives, FP denotes false positives and FN denotes false negatives. Using the formula: Specificity = number of true negativesnumber of true negatives + number of false positives*100%[43] and Sensitivity = number of true positivesnumber of true positivies + number of false negatives*100%[43]. A specificity of 100% means that the test recognizes all actual negatives[43] and a sensitivity of 100% means that the test recognizes all actual positives[43]. Positive predictive value denotes positive test results which are correctly diagnosed and Negative predictive value denotes negative test results which are correctly diagnosed. NoFuzzy comparing testing1LabelErrorFuzzy comparing testing2LabelErrorFuzzy comparing testing3LabelError101101101120110110113102Error01101140110110115011102Error0116102Error011102Error70110110118102Error0110119102Error0110111010210210211102102102121021021021310210210214011Error10210215102011Error10216102102011Error1710210210218102102011Error Table 3.5.1a: testing1, testing2 and testing3 data output using fuzzy classifier Fuzzy comparing testing1 POSITIVENEGATIVE POSITIVETP = 5FP = 4Positive predictive value = TP / (TP + FP) = 5 / (5 + 4) = 5 / 95 / 9 * 100 = 55.6%NEGATIVEFN = 1TN = 8Negative predictive value = TN / (FN + TN)= 8 / (1 + 8)= 8 / 98 / 9 * 100 = 88.9% Sensitivity = TP / (TP + FN) = 5 / (5 + 1) = 5 / 6Specificity= TN / (FP + TN) = 8 / (4 + 8) = 8 / 12 5 / 6 * 100 = 83.3%8 / 12 * 100 = 66.7% Table 3.5.1b: testing1 data output calculation using fuzzy classifier Fuzzy comparing testing2POSITIVENEGATIVEPOSITIVETP = 8FP = 1Positive predictive value = TP / (TP + FP) = 8 / (8 + 1) = 8 / 98 / 9 * 100 = 88.9%NEGATIVEFN = 1TN = 8Negative predictive value = TN / (FN + TN)= 8 / (1 + 8)= 8 / 98 / 9 * 100 = 88.9%Sensitivity = TP / (TP + FN) = 8 / (8 + 8) = 8 / 16Specificity= TN / (FP + TN) = 8 / (1 + 8) = 8 / 98 / 16 * 100 = 50%8 / 9 * 100 = 88.9% Table 3.5.1c: testing2 data output calculation using fuzzy classifier Fuzzy comparing testing3POSITIVENEGATIVEPOSITIVETP = 8FP = 1Positive predictive value = TP / (TP + FP) = 8 / (8 + 1) = 8 / 98 / 9 * 100 = 88.9%NEGATIVEFN = 2TN = 7Negative predictive value = TN / (FN + TN)= 7 / (2 + 7)= 7 / 97 / 9 * 100 = 77.8%Sensitivity = TP / (TP + FN) = 8 / (8 + 2) = 8 / 10Specificity= TN / (FP + TN) = 7 / (1 + 7) = 7 / 88 / 10 * 100 = 80%7 / 8 * 100 = 87.5% Table 3.5.1d: testing3 data output calculation using fuzzy classifier 3.5.2GAUSSIAN MIXTURE MODEL (GMM) A Gaussian Mixture Model (GMM) is a parametric probability density function represented as a weighted sum of Gaussian component densities. GMMs are commonly used as a parametric model of the probability distribution of continuous measurements or features in a biometric system[11]. A GMM is a weighted sum of Mcomponent Gaussian densities as given by the equation: pxλ=i=1Mwig(x|μi,i) where x is a D-dimensional continuous-valued data vector, wi, i=1,…,M, are the mixture weights, and g(x|μi,i), i=1,…,M, are the component Gaussian densities. Each component density is a D-variate Gaussian function of the form: g(x|μi,i)=1(2π)D2|i|12exp{-12(x-μi)'i-1x-μi}, with mean vector μiand covariance matrix i. The mixture weights satisfy the constraint that i=1Mwi=1[11]. Figure 3.5.2 shows the GMM classification method. Table 3.5.2a shows the output of 3 set of testing data from GMM classifier. Column No of incorrect normal data denotes false positives and there are 2 incorrect normal data in testing1. Column No of incorrect abnormal data denotes false negatives and there are 6 incorrect abnormal data in testing1 and testing3. The Classification rate denotes percentage of correct data. The higher the classification rate, the higher the accuracy. Table 3.5.2b-d shows GMM testing data for positive predictive value, negative predictive value, sensitivity and specificity calculation. TP denotes true positives, TN denotes true negatives, FP denotes false positives and FN denotes false negatives. Using the formula: Specificity = number of true negativesnumber of true negatives + number of false positives*100%[43] and Sensitivity = number of true positivesnumber of true positivies + number of false negatives*100%[43]. A specificity of 100% means that the test recognizes all actual negatives[43] and a sensitivity of 100% means that the test recognizes all actual positives[43]. Positive predictive value denotes positive test results which are correctly diagnosed and Negative predictive value denotes negative test results which are correctly diagnosed. Figure 3.5.2: Block diagram of GMM methodNormalized dataGMMClassifierOutputTestTrain No of correct normal dataNo of incorrect normal dataNo of correct abnormal dataNo of incorrect abnormal dataClassification rateGMM comparing testing1726372.2%GMM comparing testing29090100%GMM comparing testing3906383.3%Average classification rate(testing1+testing2+ testing3) / 3 = 85.2% Table 3.5.2a: testing1, testing2 and testing3 data output using GMM classifier GMM comparing testing1POSITIVENEGATIVEPOSITIVETP = 7FP = 2Positive predictive value = TP / (TP + FP) = 7 / (7 + 2) = 7 / 97 / 9 * 100 = 77.8%NEGATIVEFN = 3TN = 6Negative predictive value = TN / (FN + TN)= 6 / (3 + 6)= 6 / 96 / 9 * 100 = 66.7%Sensitivity = TP / (TP + FN) = 7 / (7 + 3) = 7 / 10Specificity= TN / (FP + TN) = 6 / (2 + 6) = 6 / 87 / 10 * 100 = 70%6 / 8 * 100 = 75% Table 3.5.2b: testing1 data output calculation using GMM classifier GMM comparing testing2POSITIVENEGATIVEPOSITIVETP = 9FP = 0Positive predictive value = TP / (TP + FP) = 9 / (9 + 0) = 9 / 9 = 19 / 9 * 100 = 100%NEGATIVEFN = 0TN = 9Negative predictive value = TN / (FN + TN)= 9 / (0 + 9)= 9 / 9 = 19 / 9 * 100 = 100%Sensitivity = TP / (TP + FN) = 9 / (9 + 0) = 9 / 9 = 1Specificity= TN / (FP + TN) = 9 / (0 + 9) = 9 / 9 = 19 / 9 * 100 = 100%9 / 9 * 100 = 100% Table 3.5.2c: testing2 data output calculation using GMM classifier GMM comparing testing3POSITIVENEGATIVEPOSITIVETP = 9FP = 0Positive predictive value = TP / (TP + FP) = 9 / (9 + 0) = 9 / 9 = 19 / 9 * 100 = 100%NEGATIVEFN = 3TN = 6Negative predictive value = TN / (FN + TN)= 6 / (3 + 6)= 6 / 96 / 9 * 100 = 66.7%Sensitivity = TP / (TP + FN) = 9 / (9 + 3) = 9 / 12Specificity= TN / (FP + TN) = 6 / (0 + 6) = 6 / 6 = 19 / 12 * 100 = 75%6 / 6 * 100 = 100% Table 3.5.2d: testing3 data output calculation using GMM classifier CHAPTER FOUR RESULTS The features such as blood vessels area, microaneurysms area, exudates area and textures corresponding to three features were extracted using the proposed algorithms and methods. Table 4a shows the results of fuzzy classification and Table 4b shows the results of GMM classification. The percentage of correct data for fuzzy classification over total data shows a significantly high percentage of 81.5% which makes a good classifier choice for the final graphical user interface (GUI). However, Table 3.5.2 shows the average GMM classification rate of 85.2% over the three testing data which makes an even better classifier choice than fuzzy. Therefore, GMM classifier will be used for the final GUI. Figure 4 shows the graphical plot for average percentage classification results for fuzzy and GMM classifiers. Testing1Testing2Testing3Total no of correct dataTotal no of incorrect data% of correct data over total dataAverageNo of correct data13161544(44 / 54) * 100 =81.5%No of incorrect data52310Positive predictive value55.6%88.9%88.9%77.8%Negative predictive value88.9%88.9%77.8%85.2%Sensitivity83.3%50%80%71.1%Specificity66.7%88.9%87.5%81% Table 4a: Fuzzy classification results Testing1Testing2Testing3Total no of correct dataTotal no of incorrect data% of correct data over total dataAverageNo of correct data13181546(46 / 54) * 100 = 85.2%No of incorrect data5038Classification rate72.2%100%83.3%85.2%Positive predictive value77.8%100%100%92.6%Negative predictive value66.7%100%66.7%77.8%Sensitivity70%100%75%81.7%Specificity75%100%100%91.7% Table 4b: GMM classification results Figure 4: Graphical plot for average percentage classification results from two classifiers 4.1GRAPHICAL USER INTERFACE (GUI) Figure 4.1: GUI Graphical User Interface or GUI is a type of user interface that allows users to interact with the program by clicking or typing. It allows the image features to display for both normal and abnormal classification. Figure 4.1 shows the print screen of the GUI, the list box shows the list of fundus images. By clicking ‘Extract Features’ button, blood vessels, microaneurysms, textures mean and textures third moment are displayed together with their areas. The corresponding patient’s data can be shown for every fundus image by clicking ‘Patient’s Data’. Clicking the ‘Diagnosis’ button will display either normal or abnormal classification. CHAPTER FIVE CONCLUSION AND RECOMMENDATION In this report, the system developed have demonstrated a reasonably accuracy of classification rate of 85.2% (GMM average classification rate) with sensitivity and specificity of 81.7% and 91.7% respectively (GMM average sensitivity and specificity). The algorithms and methods used for significance test and classification were fairly fast in computation speed; a good choice for comparing and computing for two classes of fundus images. The results have also demonstrated that the system can help to detect diabetic retinopathy (DR) at early stage for any DR abnormalities. This is important for ophthalmologist to detect DR and perform necessary treatments to prevent or delay vision loss. However, the system can be improved further by using more than two classifiers to improve sensitivity and specificity, more input features, diverse demographics and most importantly, the quality of the original fundus images (ie: even background illumination) need to be improved to show more detailed features as well as to improve the overall accuracy for significance test and classification. CHAPER SIX REFLECTIONS Doing capstone project has been a whole new, exciting and ‘thrilling’ experience for me. Although I’ve learned quite a lot from biomedical engineering degree, however, my choice of capstone project made me felt all awed and bewildered at the beginning. I had no prior knowledge or experience in MATLAB programming nor did I know anything about image processing. I began to doubt whether I could complete my project successfully. In order to begin my project, I needed firstly, to know more about diabetes and diabetic retinopathy, the complication of diabetes. By finding more information from the internet, journals and as well as books, I gained better understanding regarding the disease. Most importantly, the literature review enabled me to start on my proposal. The greatest hurdle was starting on MATLAB programming. I needed to find materials and information to practice on programming. I had to juggle between practicing the programming and reading the journals. Lastly, I needed to start writing the image processing codes. I spent most of my time practicing on MATLAB programming and understanding simple debugging. It was difficult to understand all codes and had to look for help from the materials as well as from my supervisor. It was quite depressing and frustrated when hitting brick walls and stuck at some point. However, it was very rewarding when I have resolved the problems. Starting on image processing was not all that smooth. Bits of problems surfaced during this time. I had to find solutions to solve / debug these coding problems. I also had to find and explore right threshold and structure element values. After some struggling and advice from my supervisor, I was able to finish feature extraction codes. Initially, I had tried and wanted to include haemorrhages feature for my project, however I was not able to implement it successfully, but it was then dawned on me that using the texture (together with the others) features to differentiate between normal and abnormal retinas were adequate as normal and abnormal retinas have different texture values. Next, I had to find out about various significant tests and with the advice from my supervisor, decided to use Student’s t-test method. I also learned about the significance p- values which features are more significant than the others, then using the data to generate normalized values for the classifiers. The learning curve for creating classifiers had been confusing and frustrating. Luckily with the help of my supervisor and Fabian, I was able to understand how to create the training and testing data for my classifiers. Lastly, I needed to learn to create a graphical user interface (GUI) for my project presentation. It was a fun and enjoyable experience which was reminiscent of my Visual Basic lessons from my poly days. All in all, the capstone project was a priceless experience for me and which I was quite satisfied with my efforts and outcomes. REFERENCES [1] 2-D median filtering – MATLAB http://www.mathworks.com/help/toolbox/images/ref/medfilt2.html. [2] Adjusting Pixel Intensity Values :: Analyzing and Enhancing Images (Image Processing Toolbox™). http://www.mathworks.com/help/toolbox/images/f11-14011.html. 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[9] Diabetic Retinopathy. http://www.hoptechno.com/book45.htm.[10] Diabetic Retinopathy Treatment - Treatment of Diabetic Retinopathy. http://vision.about.com/od/diabeticretinopathy/a/Diabetic_Retinopathy_Treatment.htm.[11] Douglas Reynolds. Gaussian Mixture Models.[12] Dr Hanno Coetzer. Morphological Image Processing Lecture 21.
[13] Eye - Wikipedia, the free encyclopedia. http://en.wikipedia.org/wiki/Eye.[14] Find edges in grayscale image – MATLAB. http://www.mathworks.com/help/toolbox/images/ref/edge.html.[15] Fovea centralis - Wikipedia, the free encyclopedia. http://en.wikipedia.org/wiki/Fovea.[16] Fundus Photography. http://www.aetna.com/cpb/medical/data/500_599/0539.html.[17] generation5 - Thresholding and Segmentation. http://www.generation5.org/content/2003/segmentation.asp.
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[27] Lens (anatomy) - Wikipedia, the free encyclopedia. http://en.wikipedia.org/wiki/Lens_(anatomy).
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[32] Ministry of Health: Disease Burden. http://www.moh.gov.sg/mohcorp/statistics.aspx?id=23712.[33] Morphological Operations. http://www.viz.tamu.edu/faculty/parke/ends489f00/notes/sec1_9.html.[34] Morphology Fundamentals: Dilation and Erosion :: Morphological Operations (Image Processing Toolbox™). http://www.mathworks.com/help/toolbox/images/f18-12508.html.
[35] Optic disc - Wikipedia, the free encyclopedia. http://en.wikipedia.org/wiki/Optic_disc.
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[38] Rajendra Acharya U, Eddie Y. K. Ng, Jasjit S. Suri. Image Modeling of the Human Eye.[39] Raman Maini, Dr. Himanshu Aggarwal. Study and Comparison of Various Image Edge Detection Techniques.[40] Ravi Jain, Ajith Abraham. A Comparative Study of Fuzzy Classification Methods on Breast Cancer Data.[41] Retina-Vitreous Center | Procedures. http://www.retinavitreouscenter.com/procedures_laser_photocoagulation.html.
[42] Scott & Christie and Associates Eye Diagram. http://www.scottandchristie.com/eye.cfm?noflash=1.[43] Sensitivity and specificity - Wikipedia, the free encyclopedia. http://en.wikipedia.org/wiki/Sensitivity_and_specificity.
[44] Singapore Association of the Visually Handicapped. http://www.savh.org.sg/info_cec_diseases.php.
[45] Stanley E. Gunstream. Anatomy and Physiology with Integrated Study Guide (3rd ed).[46] Student's t-Tests. http://www.physics.csbsju.edu/stats/t-test.html.
[47] U R Acharya, C M Lim, E Y K Ng, C Chee and T Tamura. Computer-based detection of diabetes retinopathy stages using digital fundus images.
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APPENDIX A Box plot for features (area) Box plot for blood vessels, exudates and microaneurysms respectively Box plot for mean, standard deviation and third moment respectively Box plot for entropy APPENDIX B BLOOD VESSELS MATLAB CODE clear all clc % Read original retinal image b = imread(‘file name'); b = imresize(b,[576 720]); % b(:,:,1) = red component, b(:,:,2) = green component, b(:,:,3) = blue % Assigning green component to g1 g1 = b(:,:,2); % Extract green component %============ Figure 3.3.1c =============% % Inverting the green component g2 = 255-g1; %============ Figure 3.3.1d =============% % Edge detection using canny method ed = edge(g2, 'canny'); %============ Border detection (NEW) =============% Border = imfill(ed,'holes'); [row col] = size(Border); for x = 2:5 for y = 100:650 Border(x,y) = 0; end end for x = 573:575 for y = 100:650 Border(x,y) = 0; end end % Morphological opening using the disk structuring element s1 = strel('disk',8); e1 = imerode(Border,s1); % Perform erosion d1 = imdilate(Border,s1); % Perform dilation f1 = d1-e1; % Border created %===============================================% %============== Blood vessel from background ===========% % Assigning new green component to g3 g3 = 255-g1; % Create new extacted green component a = adapthisteq(g3); % Perform adaptive histogram equalization %============ Figure 3.3.1e =============% s2 = strel('ball',8,8); % Perform morphological opening operation with structuring element 'ball' e2 = imerode(a,s2); d2 = imdilate(e2,s2); %============ Figure 3.3.1f =============% f2 = a-d2; % Subtract from original image to show blood vessels vividly %============ Figure 3.3.1g =============% th = ~im2bw(f2,0.1); %============ Figure 3.3.1h =============% mf = medfilt2(th,[3 3]); % Perform median filtering to lessen noise %============ Figure 3.3.1i =============% f3 = mf-f1; % Image with boundary attained Ifill = imfill(f3,'holes'); %Fill holes NOT touching edge for x = 1:50 % eliminate top border for y = 1:80 f3(x,y) = 1; end end %================= Calculate area =================% H = Ifill+f1; Final = unwanted(H); %final image figure, imshow(Final); %============ Figure 3.3.1j =============% Final1 = ~Final; figure, imshow(Final1); %============ Figure 3.3.1k =============% % Area Calculation L = 0; for i = 1:size(Final) for j = 1:size(Final) if Final(i,j) == 0 L = L+1; end end end L APPENDIX C MICROANEURYSMS MATLAB CODE clear all clc % Read original retinal image mi1 = imread('file name'); mi1 = imresize(mi1,[576 720]); % mi1(:,:,1) = red component, mi1(:,:,2) = green component, mi1(:,:,3) = blue r1 = mi1(:,:,1); % Extract red component %============ Figure 3.3.2c =============% % Inverting the red component r2 = 255-r1; %============ Figure 3.3.2d =============% % Edge detection using canny method ed = edge(r2,'canny'); %============ Figure 3.3.2e =============% [row col] = size(ed); for x = 2:5 for y = 100:650 ed(x,y) = 1; end end for x = 573:575 for y = 100:650 ed(x,y) = 1; end end %============= Border detection (NEW) =============% Border = imfill(ed,'holes'); s1 = strel('disk',5); e1 = imerode(Border,s1); % Perform erosion with disk of radius = 5 d1 = imdilate(Border,s1); % Perform dilation with disk of radius = 5 f1 = e1+(~d1); % Border created %============ Figure 3.3.2f =============% %===============================================% G = f1-(~ed); % Edge detection without border %============ Figure 3.3.2g =============% K = imfill(G,'holes'); % Fill holes %============ Figure 3.3.2h =============% P = K - ed; % With unwanted artifacts %============ Figure 3.3.2i =============% %============= Blood vessel detection =============% t3 = adapthisteq(r2); se = strel('ball',8,8); BW4 = imerode(t3,se); BW5 = imdilate(BW4,se); Im = t3-BW5; BW3 =~ im2bw(Im,0.08); B = BW3-(~f1); Ifill = imfill(B,'holes'); %============ Figure 3.3.2j =============% L = im2double(Ifill); L1 = edge(L,'canny'); %============ Figure 3.3.2k =============% %===================================================% %================ Final improvisations =====================% K = G-L1; %============ Figure 3.3.2l =============% Final = imfill(K,'holes'); %============ Figure 3.3.2m =============% Final2 = Final-(~P); figure, imshow(Final2); %============ Figure 3.3.2n =============% % Area Calculation L=0; for i=1:size(Final2) for j=1:size(Final2) if Final2(i,j) == 1 L=L+1; end end end L APPENDIX D EXUDATES MATLAB CODE clear all clc % Read original retinal image ex1=imread('file name'); ex1=imresize(ex1,[576 720]); % ex1(:,:,1) = red component, ex1(:,:,2) = green component, ex1(:,:,3) = blue % Assigning green component to g1 g1 = ex1(:,:,2); % Extract green component %============ Figure 3.3.3c =============% % Morphological opening using the octagon structuring element s1 = strel('octagon',9); imc = imclose(g1,s1); % Morphological closing %============ Figure 3.3.2d =============% imc = double(imc); fun = @var; im2 = uint8(colfilt(imc,[11 11],'sliding',fun)); %============ Figure 3.3.2e =============% th = im2bw(im2,0.7); %============ Figure 3.3.2f =============% s2 = strel('disk',10); d1 = imdilate(th,s2); %dilation e1 = imerode(d1,s2); %erosion %============ Figure 3.3.2g =============% ed = edge(uint8(e1),'canny'); %============ Figure 3.3.2h =============% %===================================================% G1 = rgb2gray(ex1); % Convert RGB image to grayscale G2 = imadjust(G1); % Adjust image intensity values % Detection of Optical Disk max_Ie = max(max(G2)); % Finding maximum value on the image [r, c] = find(G2 == max_Ie); Rmed = median(r); Cmed = median(c); R = floor(Rmed); C = floor(Cmed); % Mask IeSizeX = 576; IeSizeY = 720; radius = 82; [x,y] = meshgrid(1:IeSizeY, 1:IeSizeX); mask = sqrt((x-C).^2 + (y-R).^2) <= radius; %============ Figure 3.3.2i =============% % Optical Disk Removal ex2 = imsubtract(e1, mask); %============ Figure 3.3.2j =============% %===================================================% g2 = 255-g1; % Image inversion bd = g2-225; bde = edge(bd,'roberts'); % Edging sq = ones(20,20); % Thickening of edges d2 = imdilate(bde,sq); fi = ex2-d2; % Subtracting edges %============ Figure 3.3.2k =============% for x = 1:10 % Eliminate top border for y = 1:720 fi(x,y) = 0 ; end end for x = 560:576 % Eliminate bottom border for y = 1:720 fi(x,y) = 0; end end for x = 1:576 for y = 1:10 % left fi(x,y) = 0 ; end end for x = 1:576 for y = 710:720 % right fi(x,y) = 0; end end s3=strel('disk',3); e2=imerode(fi,s3); % Erosion %============ Figure 3.3.2l =============% % Area Calculation L=0; for i=1:size(fi) for j=1:size(fi) if fi(i,j)==1 L=L+1; end end end L APPENDIX E TEXTURES MATLAB CODE function [output] = firstOrderStat(image) x = rgb2gray(image); %convert to grayscale. %calculate mean mean = 0; for k = 1:255 mean = mean + k*intenProb(k,x); end %calculate Standard Deviation stddev = 0; y = int16(x)-int16(mean); % convert from uint8 to int16 to avoid overflow. z = y.*y; stddev = sum(sum(z)); stddev = stddev/numel(x); stddev = sqrt(stddev); %calculate third moment thirdMoment = 0; total = (y./stddev).^3; %(x(i)- mean)^3 thirdMoment = sum(sum(total)); thirdMoment = thirdMoment/numel(x); %divide by number of element N; %calculate entropy temp = 0; entropy = 0; for k=1:255 temp = (k-mean)*(k-mean)*intenProb(k,x); if(intenProb(k,x) ~= 0) entropy = entropy + intenProb(k,x)*log(intenProb(k,x)); end end entropy = -1*entropy; %print output output = struct('Mean',mean,'Deviation', stddev, 'Third_Moment',thirdMoment,'Entropy', entropy); end function out = intenProb(i,x) %function h(i) first Order Statistic. numOccur = 0; numOccur = sum(sum(x==i)); out = numOccur/numel(x); end APPENDIX F MEETING LOGS Capstone project meeting log - 11Date16 January 20102Time12pm – 12.30pm3Duration½ hour4Minutes of current meetingOverview of diabetes and diabetic retinopathy.5Action items/ Targets to achieveFind and read some related journals and online information regarding diabetic retinopathy and the stages and diabetes. Capstone project meeting log - 21Date6 February 20102Time11.15am – 11.45am3Duration½ hour4Minutes of current meetingDiscussed further about individual DR features such as blood vessels, exudates, microaneurysms and textures of normal and abnormal (DR) in different stages. Overview of detection of DR based on the features data and values using MATLAB.5Action items/ Targets to achieveContinue on literature review. Gained better understanding and had rough idea on how to proceed on my proposal. Capstone project meeting log - 31Date13 February 20102Time10.45am – 11.45am3Duration1 hour4Minutes of current meetingOverview of some of the MATLAB commands. I am required to practice using the MATLAB commands to prepare for writing image processing codes. I am also required to begin on my project proposal.5Action items/ Targets to achieveStarting on proposal and practice MATLAB commands. Submitted proposal draft to supervisor for vetting before submission. Capstone project meeting log - 41Date13 March 20102Time5pm – 5.30pm3Duration1/2 hour4Minutes of current meetingUpdated my ongoing MATLAB practice progress. Starting to write blood vessels extraction MATLAB codes.5Action items/ Targets to achieveContinue on literature review. Ongoing MATLAB practice. Capstone project meeting log - 51Date17 April 20102Time11am – 11.30am3Duration1/2 hour4Minutes of current meetingUpdated my ongoing MATLAB practice progress and blood vessels codes.5Action items/ Targets to achieveContinue on literature review. Ongoing MATLAB practice. Starting on interim report. Submitted interim report draft to supervisor for vetting before submission. Capstone project meeting log - 61Date8 May 20102Time11am – 11.30am3Duration1/2 hour4Minutes of current meetingReported some problems with MATLAB codes / function (threshold value and structuring elements) on blood vessels. Received advice on finding the threshold value.5Action items/ Targets to achieveContinue on literature review. Ongoing MATLAB practice. Discover the appropriate threshold value and SE value. Capstone project meeting log - 71Date22 May 20102Time11.15am – 11.45am3Duration1/2 hour4Minutes of current meetingFinished getting the average threshold value and structuring elements value. Problems with area of blood vessels values were resolved. 5Action items/ Targets to achieveContinue on literature review. Ongoing MATLAB practice. Starting on microaneurysms and exudates feature extraction coding. Capstone project meeting log - 81Date26 June 20102Time11.30am – 12pm3Duration1/2 hour4Minutes of current meetingDiscussed about microaneurysms and exudates feature extraction codes. Overview of getting the p-values (statistically significant) for different features on all images.5Action items/ Targets to achieveContinue on literature review. Ongoing MATLAB practice. Explore and discover the best way to obtain p-values for different features on all images. Capstone project meeting log - 91Date10 July 20102Time11.10am – 11.40am3Duration1/2 hour4Minutes of current meetingDiscussed about texture feature extraction.5Action items/ Targets to achieveContinue on literature review. Ongoing MATLAB practice. Starting to write texture feature extraction codes. Capstone project meeting log - 101Date31 July 20102Time11.00am – 11.30am3Duration1/2 hour4Minutes of current meetingDiscussed about texture feature extraction codes. Overview of classifier and using different classifiers to generate results.5Action items/ Targets to achieveContinue on literature review. Ongoing MATLAB practice. Starting to write classifier codes and preparing training and testing data. Capstone project meeting log - 111Date14 August 20102Time11.00am – 11.30am3Duration1/2 hour4Minutes of current meetingDiscussed about classifiers results and overview of creating graphical user interface (GUI).5Action items/ Targets to achieveStarting to write GUI codes. Capstone project meeting log - 121Date21 August 20102Time11.00am – 11.30am3Duration1/2 hour4Minutes of current meetingPresenting GUI to supervisor.5Action items/ Targets to achievePreparing the materials to start writing final report. APPENDIX G GANTT CHART

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