Significant point characterization in fundus images

This paper describes a new approach to identify significant points in retinal images. Significant points such as bifurcations and crossovers define and characterize the retinal vascular network. This approach is based on using hit-or-miss transformation to detect terminal, bifurcation and simple crossing points and performs a post-processing stage to identify complex intersections. The post-processing focuses on the idea that the intersection of two vessels creates a sort of close loop formed by the vessels and this effect can be used to differentiate a bifurcation and a crossover. Experimental results show quantitative improvements if the proposed method is compared with other state-of-the-art work by reducing the number of false positives and negatives in the significant point detection. Therefore, the result of this work is an effective significant point detection algorithm and can be useful for cardiovascular disease diagnosis, biometrics and image registration.