Detection of Eye Disorders Through

Retinal Image AnalysisBlood Vessel Segmentation, Optic Disc

Segmentationand Fuzzy Logic Image Processing

ByRahul Dey

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Overview of the Presentation

Common Eye Disorders

Blood and Optic disk Segmentation Literature Survey Algorithm Simulation

Fuzzy Logic Image Processing Introduction Fuzzy Inference System Implementation on Edges

3Common Retinal Eye Disorders

Fig .2 GlaucomaFig.3 Diabetic Retinopathy

Source : www.fau.de

• Glaucoma is associated with elevated pressure in eye which damages optic nerve

• DR is a common retinal complication associated with diabetes

Fig.1 Normal Eye

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Literature Survey For Optic Disk Segmentation

Extraction of optic disk, fovea, and blood vessel are used for comprehensive analysis and grading of diabetic retinopathy

Other symptoms which can be detected are cotton wool spot, Microaneurysms and haemorrages.

Methods : Circular hough transformation for detection optic disk Curvlet transformation Artificial neural network

Source : www.fau.de

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Literature Survey For Blood Vessel Segmentation

Blood vessel provides nourishment to retina while diabetes may weaken and leak blood vessel forming dot like haemorrages

These leaking vessels often lead to swelling and decreased vision

Blood vessels are segmented to locate optic disk and fovea

Fig.4 Segmented Blood vessel

6Algorithm

Read image & set threshold for , blood vessel segmentation and optic disk dilating window

Blood vessel segmentation starts Resize image to 576 576 Read green channel because green channel has the highest

contrast Performing morphological operation to highlight blood vessel

having size 1 to 6 Adaptive histogram equalization Gaussian filtering ( Median filtering having kernel size 2 x 2 Binarization with user defined threshold

7Algorithm (contd..) Thinning operation Median filtering having kernel size 2 x 2 Filling and dilation

End of blood vessel segmentation

Optic disk segmentation starts Read image Extract red plane Extract green plane Read template of user defined size Extract red plane of the template Do normalize correlation

8Algorithm (contd..)

If Correlation co-efficient is > user defined threshold then extract

optic disk Dilate extracted optic disk with square window of 3 x 3 to 4 x 4 Take mean value of the dilated image as threshold Binarize the image Median filter of size 3 x 3 to 4 x 4 Open by taking kernel size of 4 x 4 Fill the image Perform close operation on the image with disk shape kernel of

radius 2 to 3 pixel Show image Canny edge detection

Else Display error message

End of Optic disk segmentation

9Simulation Output

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Difference Between Proposed & Our Methodology

Proposed Our• Gaussian filtering is not done • Additional Gaussian filtering

done

• Green channel used for optic disk localization

• Red channel used for optic disk localization

• Green channel used for optic disk segmentation

• Red channel used for optic disk segmentation

• Filtering kernel size information are missing

• All filtering sizes are computed

11Blood Vessel Result Comparison

12Optic Disk Comparison

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Fuzzy Logic Image Processing(FLIP)

Fuzzy image processing is a combination of fuzzy approach to image processing.

Fuzzy image processing stages:

After the image data are transformed from gray-level plane to the membership plane (fuzzification),

appropriate fuzzy techniques modify the membership values.

14Applications of Fuzzy Logic Image Processing

Contrast Enhancement

Edge Detection

Noise Detection and Removal

Segmentation

Geometric measurement

Scene analysis (Region Labelling)

15Retinal Image Analysis using Fuzzy Logic

One of the main aspects in Retinal Image Analysis is Edge detection of the Blood Vessels Network in the retinal images

We enhanced the appearance of blood vessel network in the segmented retinal images through various edge detection techniques.

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Fuzzy Inference System Fuzzy inference is the process of formulating the

mapping from a given input to an output using fuzzy logic.

In order to compute the output of a given FIS from the inputs, these five steps should be done:o Fuzzifying InputsoApplying Fuzzy OperatorsoApplying Implication MethodsoAggregating all outputsoDefuzzifying

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Proposed Fuzzy Inference System

Mamdani FIS by taking a movable window over the image of 2x2 size.

Inputs: Two of them, which are the gradients with respect to x-axis and y-axis.

Output: Edges

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Proposed Fuzzy Inference System

For both the fuzzy variables, the membership functions are Gaussian which are: LOW: gaussmf(43,0) MEDIUM: gaussmf(43,127) HIGH: gaussmf(43,255)

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Proposed Fuzzy Inference System

Rules If (DH is LOW) and (DV is LOW) then (EDGES is LOW) If (DH is MEDIUM) and (DV is MEDIUM) then (EDGES is

LOW) If (DH is HIGH) and (DV is HIGH) then (EDGES is HIGH)

Output

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Simulation Output Edge Detection through Canny Method

Canny Fuzzy Canny

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Simulation Output Edge Detection through Sobel Method

Sobel Fuzzy Sobel

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Simulation Output Edge Detection through Prewitt Method

Prewitt Fuzzy Prewitt

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Simulation Output Edge Detection done through our project code

Project code Fuzzy Project Code

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