Automatic Detection of Diabetic Maculopathy from Fundus Images Using Image Analysis Techniques

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 intwo 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 sensorytissue that lies on the back of the eye. It capture light rays andconvert them into electrical impulses that travel along the opticnerve to the brain where they are turned into image.

Physiology of Retina

Optic disk

Macula

Fovea

Vascular NetworkOptic Disk Macula

Fovea

Vascular Network

Abnormal Lesion of Diabetic Retinopathy

Exudates

Hard Exudates

Soft Exudates

Diabetic Maculopathy

Diabetic Maculopathy occurs if exudates appear near themacula affecting central vision stage.

Normal eye Eye with Diabetic Maculopathy

Diabetic Maculopathy

The severity level of Diabetic Maculopathy is classified in to:-

Sever

Moderate

Mild1/3DD

1DD

2DD

Color Bands Analysis

The input images has orange dominate color which indicatesthat the blue channel doesn't have significant information.

Fundus Region Detection

Improve the efficiency of the system by extracting backgroundand removing the damaged areas from retinal image toallocate 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 anddark background surrounding it which is not clear homogenousblack area. The background area is omitted using :-Thresholding, Dilation, Mask Generation.

Damaged Areas Segmentation

Damaged or non-informatics areas in color retinal imageis usually due to pixels whose color is distorted; they existin some parts of the fundus boundary regions whereillumination was not inadequate.

Caused by a number of factors, including retinalpigmentation, 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 andremoved.

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

𝐑𝐞𝐝 + 𝐆𝐫𝐧 ≤ 𝐓𝐡𝐫𝟐𝐚𝐧𝐝𝐆𝐫𝐧

𝐑𝐞𝐝≥ 𝐓𝐡𝐫𝟑𝐚𝐧𝐝

𝐆𝐫𝐧/𝐑𝐞𝐝 ≤ 𝐓𝐡𝐫𝟒

Seed Filling Algorithm

Remove the areas of white patches that may appear in theresulted binary image, which are considered as poor imagequality areas.

Localization of ROI

Allocate the actual region in the retinal image and flag itspixels from other areas pixels in the ocular fundus image.

𝐆𝐚𝐩𝐬 𝐅𝐢𝐥𝐥𝐢𝐧𝐠 𝐚𝐧𝐝 𝐍𝐨𝐢𝐬𝐞 𝐑𝐞𝐦𝐨𝐯𝐚𝐥 𝐄𝐝𝐠𝐞 𝐒𝐦𝐨𝐨𝐭𝐡𝐢𝐧𝐠𝐑𝐞𝐦𝐨𝐯𝐢𝐧𝐠 𝐨𝐟 𝐒𝐦𝐚𝐥𝐥 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥

𝐆𝐚𝐩𝐬 𝐚𝐧𝐝 𝐏𝐨𝐫𝐞𝐬

Allocate the Most InformaticColor Band

Green Channel image shows better contrast than the redchannel. It is observed that the anatomical and pathalogyfeatures appears most contrasted in green channel in RGBimage.

Allocate the Most InformaticColor Band

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61

76

91

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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 thespot on the retina where the optic nerve and blood vesselsenter the eye.

Specify the image, where it is for the left or right side.

Cup the disk ratio is commonly used to assess the glaucomadisease.

Location of OD can be a target point for identify theposition of the macula.

OD is masked when detection of exudates to prevent thefalse 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-linearGamma 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 theretina as darker small yellowish area adjacent to the optic diskabout (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 canbe determined in the whole macular region.

Detection of Normal Features (Macula & Fovea)

Locating the Macula.

Detection of Normal Features (Macula & Fovea)

Non-LinearGamma 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 diseasedvessels can deposited in the macular region leading tothe visual distortion.

The common feature in hardand soft exudates lesions isthat they both appear asbrighter areas relative totheir 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 brightnessand size Feature

3 5

2 6

6

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 severitylevel, the macular regions are divided into three circularareas R1, R2 and R3 centered at fovea. Where R1 isrepresent the sever region, while R2 for the moderateregion 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/Absent

Mild 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 availabledatasets of color retinal image DIARETDB0 which contains130 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 imagequality in the localized region of interest.

Results

93.49% accuracy rate in detecting optic disk. The Sensitivityand specificity of detection achieved 92.68% and 100%,respectively.

Results

94.69% accuracy rate in detecting macula region. TheSensitivity and specificity of detection achieved 94.17% and100%, respectively.

100% accuracy rate in detecting fovea.

Results

87.62% accuracy rate in detecting optic disk. The Sensitivityand specificity of detection achieved 88.46% and 86.66%,respectively.

Thanks For Your !Attention