Segmentation and classification of FMM compressed retinal images using watershed and canny segmentation and support vector machine

Akshay, S. and Apoorva, P. (2017) Segmentation and classification of FMM compressed retinal images using watershed and canny segmentation and support vector machine. In: 2017 International Conference on Communication and Signal Processing (ICCSP).

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Abstract

Diabetic retinopathy is an ailment of the retinal vasculature that ultimately develops to some diploma in nearly all patients with lengthy-status diabetes. Proliferative diabetic retinopathy is an uncommon circumstance in all likelihood to cause acute visual deficiency. It is observed via the growth of unusual new retinal vessels. To symbolize the improvement of irregular new retinal vessels, an algorithm for spontaneously identifying new vessels on the optic disc using retinal photographs is described. The algorithm takes Five module method (FMM) compressed retinal images as the input. Watershed lines and canny detectors are used to find the vessel like candidate segment. Different features namely shape of the segment, position of the segment from the origin, positioning, intensity of the segment in the image, divergence, and line density are extracted for each candidate segment. Each candidate segment is labeled as normal or abnormal based on its features using Support Vector Machine (SVM) classifier. The experimentation results suggests that the automated retinopathy analysis system provides clinical insights in detecting the ailment.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: biomedical optical imaging;blood vessels;diseases;eye;feature extraction;image classification;image segmentation;medical image processing;support vector machines;retinal vessels;image segmentation;image classification;five module method;canny detectors;watershed lines;retinal photographs;acute visual deficiency;proliferative diabetic retinopathy;lengthy-status diabetes;retinal vasculature;canny segmentation;FMM compressed retinal images;automated retinopathy analysis system;Support Vector Machine classifier;Retina;Image segmentation;Image coding;Optical imaging;Support vector machines;Lenses;Feature extraction;Five modules Method;Watershed lines;Canny edge detection;Support Vector Machine;Retinal image;Optic disc;Optic vessel
Subjects: D Physical Science > Computer Science
Divisions: Department of > Computer Science
Depositing User: lpa manjunath user
Date Deposited: 16 Oct 2019 09:36
Last Modified: 16 Oct 2019 09:36
URI: http://eprints.uni-mysore.ac.in/id/eprint/9071

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