Detection of Neovascularization in Proliferative Diabetic Retinopathy Fundus Images

Neovascularization is a serious visual consequence disease arising from Proliferative Diabetic Retinopathy (PDR). The condition causes progressive retinal damage in persons suffering from Diabetes mellitus, and is characterized by busted growth of abnormal blood vessels from the normal vasculature, which hampers proper blood flow into the retina because of oxygen insufficiency in retinal capillaries. The present paper aims at detecting PDR neovascularization with the help of the Adaptive Histogram Equalization technique, which enhances the green plane of the fundus image, resulting in enrichment of the details presented in the fundus image. The neovascularization blood vessels and the normal blood vessels were both segmented from the equalized image, using the Fuzzy C-means clustering technique. Marking of the neovascularization region, was achieved with a function matrix box based on a compactness classifier, which applied morphological and threshold techniques on the segmented image. Subsequently, the Feed Forward Back-propagation Neural Network interacted with extracted features (e.g., number of segments, gradient variation, mean, variance, standard deviation, contrast, correlation, entropy, energy, homogeneity, cluster shade towards the neovascularization detection region), in an attempt to achieve accurate identification. The above method was tested on images from three online datasets, as well as two hospital eye clinics. The performance of the detection technique was evaluated on these five image sources, and found to show an overall accuracy of 94.5% for sensitivity of 95.4% and of specificity 49.3% respectively, thus reiterating that the method would play a vital role in the study and analysis of Diabetic Retinopathy.