Retinal macular edema severity detection based on radial symmetry depend NN classifier

Diabetic macular edema (DME) is an advanced symptom of diabetic retinopathy and can lead to irreversible vision loss. In this paper, a two-stage methodology for the detection and classification of DME severity from color fundus images is proposed. DME detection is carried out via a supervised learning approach using the normal fundus images. A feature extraction technique is introduced to capture the global characteristics of the fundus images and discriminate the normal from DME images. Exudates are one of the visible signs of diabetic retinopathy and a marker for the presence of coexistent retinal edema. Automatic exudates detection would be useful in order to detect and treat diabetic retinopathy in an early stage. In this project a method for automatic detection of optic disc followed by classification of hard exudates pixels in retinal image. Optic disc localization is achieved by Radial symmetry transform (RST) to identify initial set of candidate regions followed by connected component analysis to locate the actual optic disc. Exudates are detected using hidden neural network based classifier. The sensitivity, specificity and accuracy are used to evaluate the performance.