Computer-assisted identification of proliferative diabetic retinopathy in color retinal images.

Abstract

Advanced (proliferative) stage of diabetic retinopathy (DR) is indicated by the growth of thin, fragile and highly unregulated vessels, neovascularization (NV). In order to identify proliferative diabetic retinopathy (PDR), our approach models the micro-pattern of local variations using texture based analysis and quantifies the structural changes in vessel patterns in localized patches, to map them to the confidence score of being neovascular using supervised learning framework. Rule-based criteria on patch-level neovascularity scores in an image is used for the decision of absence or presence of PDR. Evaluated using 3 datasets, our method achieves 96% sensitivity and 92.6% specificity for localizing NV. Image-level identification of PDR achieves high sensitivity of 96.72% at 79.6% specificity and high specificity of 96.50% at 73.22% sensitivity. Our approach could have potential application in DR grading where it can localize NVE regions and identify PDR images for immediate intervention.