Segmentation of Retinal Blood Vessels Using Pulse Coupled Neural Network to Delineate Diabetic Retinopathy

Diabetic Retinopathy (DR) is the root cause for retinal blood vessel damages among the diabetic patients. If it is not identified and treated earlier, at the later stage it leads to 100% vision loss. Thus there is a need of a system to identify the early stage of DR, so that it can be treated according to ETDRS (Early Treatment Diabetic Retinopathy Study). The proposed Pulse Coupled Neural Network (PCNN) model segments the retinal blood vessels from the depigmented fundus images and provides the structure of the retinal blood vessels. This segmented blood vessel map helps the ophthalmologist to identify the severity level of the blood vessel damages and to treat the early Diabetic Retinopathy among different age group populations. The proposed PCNN model is applied over the DRIVE database and the results are compared with various supervised and unsupervised segmentation approaches. The proposed method improves the accuracy in detecting the tiny blood vessels in the depigmented fundus images than other existing methods. This system increases the number of true positives; true negatives and reduces the false positives, false negatives while compared with the ground truth images. The Specificity of the proposed system over DRIVE database is 99.31%, Sensitivity is 67.54% and Accuracy is 97.23%. The resultant image of the segmented blood vessels can be used for further diagnosis and to measure the severity level of DR.