Retinal Vessel Classification Using the Non-local Retinex Method

Abstract

Automatic retinal vessel segmentation has turned out to be highly propitious for medical practitioners to diagnose diseases like glaucoma and diabetic retinopathy. These diseases are classified based on the thickness of the retinal vessel, the pressure imposed on the nerve endings and optical disc to cup ratio of the retina. The state-of-the-art device for this purpose presently available in the market is expensive and has scope to meliorate sensitivity and precision of its performance. Thus, automatic retinal blood vessel segmentation and classification is the need of the hour. In this paper, a novel non-local total variational retinex based retinal image preprocessing approach is proposed to extract the retinal vessel features and classify the vessels using ground truth images. Matlab implementation results indicate that an average accuracy of 94% with an acceptable range of sensitivity and specificity could be achieved on the retinal image database available online. © 2020, Springer Nature Switzerland AG.