Automatic Detection of Diabetic Maculopathy from Fundus Images Using
Image Analysis Techniques.
Submitted By-: Eman Abdulalazeez Gani Aldhaher
1436-2014
The Human Eye
Eye is an organ associated with vision. The abnormalities associated with the eye can be divided in
two main classes:- Eye diseases such as:- cataracts, glaucoma.Life style related disease such as:- hypertension, diabetes.
Diabetes Mellitus
Diabetes is a chronic metabolic disorder caused by either the pancreas either produced too little or no insulin or the cells do not react properly to the insulin that is produced.
Diabetes can harm eye by damaging blood vessels of eye retina, which in turn can cause loss of vision.
According to World Health Organization (WHO), number of adults with diabetes in the world would increase alarmingly from 135 million in 1995 to 483 million in 2030.
The Human Retina
The retina, also called fundus image, is a multi-layered sensory tissue that lies on the back of the eye. It capture light rays and convert them into electrical impulses that travel along the optic nerve to the brain where they are turned into image.
Physiology of Retina
Optic disk Macula Fovea Vascular Network
Optic Disk Macula
Fovea
Vascular Network
Abnormal Lesion of Diabetic Retinopathy
Exudates Hard Exudates Soft Exudates
Diabetic Maculopathy
Diabetic Maculopathy occurs if exudates appear near the macula affecting central vision stage.
Normal eye Eye with Diabetic Maculopathy
Diabetic Maculopathy
The severity level of Diabetic Maculopathy is classified in to:- Sever Moderate Mild
1/3DD1DD
2DD
Color Bands Analysis
The input images has orange dominate color which indicates that the blue channel doesn't have significant information.
Fundus Region Detection
Improve the efficiency of the system by extracting background and removing the damaged areas from retinal image to allocate the actual region of interest (ROI).
Background Area
Background Area
Damaged Areas
Damaged Areas
Background Elimination
A color retinal image consist of a semi circular fundus and dark background surrounding it which is not clear homogenous black area. The background area is omitted using :- Thresholding, Dilation, Mask Generation.
Damaged Areas Segmentation
Damaged or non-informatics areas in color retinal image is usually due to pixels whose color is distorted; they exist in some parts of the fundus boundary regions where illumination was not inadequate.
Caused by a number of factors, including retinal pigmentation, acquisition angle, inadequate illumination, cameras' differences and patient movement.
Poor image quality region are detected using three steps:- Max-Min Detector. Ratio Detector Seed Filling Algorithm
Max-Min Detector
Region of inadequate image quality will be detected and removed.
His (gray level)
Pr (gray level)
Min:- Pr ≥ Val1*SizMax:- Prmax ≥ Val2*Siz
Min≤ Pixel value ≥Max
Min≤ Pixel value ≥Max
Ratio Detector
Extract the blurred regions from the retinal image.
𝐑𝐞𝐝+𝐆𝐫𝐧<𝐓𝐡𝐫𝟏
Seed Filling Algorithm
Remove the areas of white patches that may appear in the resulted binary image, which are considered as poor image quality areas.
Localization of ROI
Allocate the actual region in the retinal image and flag its pixels from other areas pixels in the ocular fundus image.
𝐆𝐚𝐩𝐬𝐅𝐢𝐥𝐥𝐢𝐧𝐠𝐚𝐧𝐝𝐍𝐨𝐢𝐬𝐞𝐑𝐞𝐦𝐨𝐯𝐚𝐥 𝐄𝐝𝐠𝐞𝐒𝐦𝐨𝐨𝐭𝐡𝐢𝐧𝐠
Allocate the Most Informatic Color Band
Green Channel image shows better contrast than the red channel. It is observed that the anatomical and pathalogy features appears most contrasted in green channel in RGB image.
Allocate the Most Informatic Color Band
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Contrast Stretching
Dif Between Adjacent Pixels
Energy of Dif
Detection of Normal Features (Optic Disk)
OD is a bright yellowish disk within the retinal image. It is the spot on the retina where the optic nerve and blood vessels enter the eye.
Specify the image, where it is for the left or right side.Cup the disk ratio is commonly used to assess the
glaucoma disease.Location of OD can be a target point for identify the
position of the macula.OD is masked when detection of exudates to prevent the
false positive.
Detection of Normal Features (Optic Disk)
Locating the Optic Disk.
Thresholding Process Seed Filling Algorithm Circle Equation Mask Locating the OD
Detection of Normal Features (Optic Disk)
Accurate Localization of the Optic Disk.
Non-linear Gamma Mapping
Seed Filling Algorithm
Determine center point
Circle Equation Mask
Localized the Optic Disk
Detection of Normal Features (Macula &
Fovea) Macula, is another part of the main components of the retina.
In a color retinal image, it appears roughly in the center of the retina as darker small yellowish area adjacent to the optic disk about (4.5 mm in diameter).
Fovea is the central part of the region of the macula. It is vital to allocate the macular region. By localization the fovea, occurrence of the maculopathy can
be determined in the whole macular region.
Detection of Normal Features (Macula &
Fovea) Locating the Macula.
Detection of Normal Features (Macula &
Fovea)
Non-Linear Gamma Mapping
Thresholding Process
Seed Filling Algorithm
Locating the Macula and its center (Fovea).
Locating the Fovea
Detection of Abnormal Features
(Exudates) Exudates is a fluid rich in fat, leaks out of diseased
vessels can deposited in the macular region leading to the visual distortion.
The common feature in hard and soft exudates lesions is that they both appear as brighter areas relative to their neighborhood.
Detection of Abnormal Features
(Exudates)
Non-Linear Gamma Mapping.
Max Filter (Dawn-Sampling).
Local Thresholding.
Remove Optic Disk.
Up-Sampling
Seed Filling Algorithm
Classify the Exudates using brightness and size Feature
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x,y x+1,y x+2,y
x,y+1 x+1,y+1 x+2,y+1
x,y+2 x+1,y+2 x+2,y+2
2x,2y 2x+1,y
2x,2y+1 2x+1,2y+1
Determining the Severity Level of Diabetic
Maculopathy To automatic grading of diabetic maculopathy severity
level, the macular regions are divided into three circular areas R1, R2 and R3 centered at fovea. Where R1 is represent the sever region, while R2 for the moderate region and R3 for the mild region.
Severity level Hard and Soft Exudates R1 R2 R3
Sever Present Present/Absent Present/Absent
Moderate Absent Present Present/AbsentMild Absent Absent Present
Determining the Severity Level of Diabetic
Maculopathy
Normal Mild Stage
Moderate Stage Sever Stage
Results
The proposed system is tested om a publically available datasets of color retinal image DIARETDB0 which contains 130 retinal image with size 1500×1152. 96.92% accuracy rate in detecting of fundus image region (background elimination).
Results
97.67% accuracy rate in detecting the regions of poor image quality in the localized region of interest.
Results
93.49% accuracy rate in detecting optic disk. The Sensitivity and specificity of detection achieved 92.68% and 100%, respectively.
Results
94.69% accuracy rate in detecting macula region. The Sensitivity and specificity of detection achieved 94.17% and 100%, respectively.
100% accuracy rate in detecting fovea.
Results
87.62% accuracy rate in detecting optic disk. The Sensitivity and specificity of detection achieved 88.46% and 86.66%, respectively.
Thanks For Your !Attention
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