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

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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