Early detection of diabetic retinopathy by CRA (Cluster rejection approach)

Abstract

Diabetic Retinopathy (DR) is a major public health issue, since it can lead to blindness in patients with diabetes. Since large number of existing method of detecting DR had given importance to robust modeling of MA (Microaneurysm) by explicit segmentation of optic disk and vessels. Existing methods of detecting MA involve complex modeling results in high computation cost and time consuming process. In order to improve efficiency of the system, proposed a new approach for detecting DR based of cluster rejection methodology for detecting MA from retinal image. The proposed technique involves cluster separation of retinal images and selecting candidate sets based on simple threshold and rejection of candidates from DR affected retinal image. Our proposed methodology for detecting MA results in easy computation of fungus image and less time consuming process.