An automated early diabetic retinopathy detection through improved blood vessel and optic disc segmentation

This paper presents an automated early diabetic retinopathy detection scheme from color fundus images through improved segmentation strategies for optic disc and blood vessels. The red lesions, microaneurysms and hemorrhages are the earliest signs of diabetic retinopathy. This paper essentially proposes improved techniques for microaneurysm as well as hemorrhages detection, which eventually contribute in the overall improvement in the early detection of diabetic retinopathy. The proposed method consists of five stages- pre-processing, detection of blood vessels, segmentation of optic disc, localization of fovea, feature extraction and classification. Mathematical morphology operation is used for pre-processing and blood vessel detection. Watershed transform is used for optic disc segmentation. The main contribution of this model is to propose an improved blood vessel and optic disc segmentation methods. Radial basis function neural network is used for classification of the diseases. The parameters of radial basis function neural network are trained by the features of microaneurysm and hemorrhages. The accuracy of the proposed algorithm is evaluated based on sensitivity and specificity, which are 87% and 93% respectively.