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

摘要

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.