diabetic retinopathy image classification
TRANSCRIPT
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Diabetic Retinopathy Image Classification
Dr. Yusheng Feng, Dr. Artyom Grigoryan, John Jenkinson
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Research Objective• Produce a scheme for the detection of
exudates in optical fundus images for the automated diagnosis of diabetic retinopathy
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Group Members• Dr. Yusheng Feng
• (Mechanical Engineering, SiViRT* Director)
• Dr. Artyom Grigoryan • (Electrical and Computer Engineering)
• John Jenkinson • (Electrical and Computer Engineering, SiViRT*)
• *SiViRT – Center for Visualization, Simulation, and Real-Time Prediction
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Original Pathological Image
Figure 1. Original Pathological RGB Image from Messidor Database
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Red Channel and Correlation Mask
This mask is correlated with each image to locate the Optic Disc
Figure 3. Correlation Mask
Figure 2. Red channel of original RGB Image
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Correlation Image and Disk Mask
Figure 4. Correlation Image with brightest point where largest correlation occurs
Figure 5. Mask created by setting pixels within 150 radius from circle center to zero
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Green Channel with Removed Disk
Figure 6. Green channel image with optic disc removed by mask
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Top-hat Transform Filter
Figure 7. Top-hat transform working as an regional adaptive threshold on the Green channel image
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Contrast Stretching Top-hat Image
Figure 8. Contrast stretched image to fill the dynamic range of the top-hat enhanced image, and example region of exudates displayed with high intensity enveloped in green
Figure 9. Region of contrast-stretched Image displayed exudate groups circled in red
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Threshold Binary Image
Figure 10. Background removed by applying an image threshold to contrast-stretched image
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Morphological Opening
Figure 11. Morphological opening with structuring element disk of size 1 for stage one noise removal (coarse objects)
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Median Filtering
Figure 12. Median filter window size 7x7 for stage 2 noise removal (fine objects)
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Border Pixel Removal
Figure 13,14. Red channel image (left) displayed with border region displayed in white. Border region is applied to Median Filtered image (right) and sets all pixels within the border to zero intensity
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Kirsch’s Edge Analysis
Figure 15,16. Kirsch’s edge analysis (left) is used to develop a probability map for exudate candidates (right)
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Exudate Candidate Map
Figure 17,18. The average value of an edge from Kirsch’s edge analysis is assigned to every pixel in that object (left) with a region of objects displayed (right)
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Probability
Figure 19. The candidate map is then scaled so that the value of each object represents its probability of being an exudate. The features of each object extracted for classification (next slide) are weighted by the value of their exudate probability
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Feature Extraction
• Density Feature: Ratio of Object Area (constant region) to Bounding Box (slashed region)
• Area, Mean Intensity, and Variance Features: The constant region represents an object. The features corresponding to this object representation are the Area: number of pixels in the object, Mean Intensity: average intensity of the Green channel pixels for the object and Variance: see Mean Intensity
• Axes Ratio Feature: The ratio of the Major axis, represented by the line AB to the Minor Axis represented by the line CD
Figure 20. Density Feature Figure 21. Area, Mean Intensity, Variance Figure 22. Axes Ratio
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Classification• Trained: Decision Trees,
Support Vector Machines, Nearest Neighbor Classifiers, and Ensemble Classifiers.
• SVM with Quadratic Kernel performed the best with 0.7238 Area under the Receiver Operating Characteristic Curve with Hold-One-Out 0.2 Threshold Validation.
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Next Steps• Improve classification performance by
adjusting feature selection and optimizing segmentation.
• Optimize segmentation for additional databases: Diaretdb1, HEI-MED, and e-optha EX
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THANK YOUQuestions? Comments?