A Probabilistic Model for the Optimal Configuration of Retinal Junctions Using Theoretically Proven Features

This paper aims to reconstruct retinal vessel trees from the broken vessel segments in fund us images for clinical studies and early diagnosis of systemic diseases including diabetic retinopathy, atherosclerosis, and hypertension. A Naive Bayes model is proposed for correct configurations of segments at retinal junctions including bifurcations, crossovers, overlaps, and mixture of these. The Maximum A Posteriori (MAP) is established to select the most likely configuration. In addition, the feature set consists of proportional associations of vessels width, angle and orientation. These theoretically proven associations are based on the optimality principles of minimum work in the vasculature for blood flow efficiency. We modelled the system using the training set of DRIVE database, tested on the testing set of same database, and produced 93.3% overall accuracy.