Research Article Support Vector Machine Based Pades Approximant for Diabetic Retinal Eye Detection

Abstract: Diabetic Retina (DR), a problem of formation of blood clot must be diagnosed at an early stage for laser therapy. A number of automated diagnosis methods based on image segmentation of fundus image is present which can diagnose DR at late mild proliferative stage. Proposed work is aimed to detect DR at early mild proliferative stage. Method uses feature extraction of fundus image using 2D Gabor filtering and pre-classification for feature vector extraction using Pades approximation. The Padesvector are then again classified using SVM by forming a dual of convex quadratic type minimization problem for linearly separable hyper plane. The performance of the proposed work is tested with set of images taken from fundus camera

Graph-Based Minimal Path Tracking in the Skeleton of the Retinal Vascular Network

This paper presents a semi-automatic framework for minimal path tracking in the skeleton of the retinal vascular network. The method is based on the graph structure of the vessel network. The vascular network is represented based on the skeleton of the available segmented vessels and using an undirected graph. Significant points on the skeleton are considered nodes of the graph, while the edge of the graph is represented by the vessel segment linking two neighboring nodes. The graph is represented then in the form of a connectivity matrix, using a novel method for defining vertex connectivity. Dijkstra and Floyd-Warshall algorithms are applied for detection of minimal paths within the graph. The major contribution of this work is the accurate detection of significant points and the novel definition of vertex connectivity based on a new neighborhood system adopted. The qualitative performance of our method evaluated on the publicly available DRIVE database shows useful results for further purposes