This document describes methods for detecting eye disorders through retinal image analysis. It discusses segmenting blood vessels and the optic disk using algorithms. It also covers applying fuzzy logic image processing to enhance edge detection of blood vessels. The proposed approach uses a Mamdani fuzzy inference system on a moving window to classify edges based on gradient inputs and Gaussian membership functions. Simulation results show the fuzzy method enhances edge detection compared to common methods like Canny, Sobel, and Prewitt.

1. Detection of Eye Disorders
Through Retinal Image
Analysis
Blood Vessel Segmentation, Optic Disc Segmentation
and Fuzzy Logic Image Processing
By
Rahul Dey

2. 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
2

3. Common Retinal Eye Disorders 3
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

4. 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
4

5. 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
5
Fig.4 Segmented Blood vessel

6. Algorithm 6
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
𝐶 = 𝐴 ⊕ 𝐵2 ⊖ 𝐵2 − (𝐴 ⊕ 𝐵1) ⊖ 𝐵1
 𝐵1, 𝐵2 having size 1 to 6
 Adaptive histogram equalization
 Gaussian filtering ( 𝜎 = 2 )
 Median filtering having kernel size 2 x 2
 Binarization with user defined threshold

7. Algorithm (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
𝑖=1
𝑀
(𝑇 𝑖− 𝑇)(𝐼 𝑖,𝑣− 𝐼)
𝑖=1
𝑀 (𝑇𝑖 − 𝑇)2
𝑖=1
𝑀 (𝐼𝑖,𝑣− 𝐼 𝑣)2
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8. Algorithm (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
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9. Simulation Output 9

10. 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
10

11. Blood Vessel Result Comparison 11

12. Optic Disk Comparison 12

13. 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.
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14. Applications of Fuzzy Logic Image
Processing
 Contrast Enhancement
 Edge Detection
 Noise Detection and Removal
 Segmentation
 Geometric measurement
 Scene analysis (Region Labelling)
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15. Retinal 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.
15

16. 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 Inputs
o Applying Fuzzy Operators
o Applying Implication Methods
o Aggregating all outputs
o Defuzzifying
16

17. 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
17

18. 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)
18

19. 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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20. Simulation Output
 Edge Detection through Canny Method
Canny Fuzzy Canny
20

21. Simulation Output
 Edge Detection through Sobel Method
Sobel Fuzzy Sobel
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22. Simulation Output
 Edge Detection through Prewitt Method
Prewitt Fuzzy Prewitt
22

23. Simulation Output
 Edge Detection done through our project code
Project code Fuzzy Project Code
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