AUTOMATIC DETECTION OF VASCULAR BIFURCATIONS AND CROSSOVERS FROM COLOR RETINAL FUNDUS IMAGES

Identifying the vascular bifurcations and crossovers in the retinal image is helpful for predicting many cardiovascular diseases and can be used as biometric features and for image registration. In this paper, we propose an efficient method to detect vascular bifurcations and crossovers based on the vessel geometrical features. We segment the blood vessels from the color retinal RGB image, and apply the morphological thinning operation to find the vessel centerline. Applying a filter on this centreline image we detect the potential bifurcation and crossover points. The geometrical and topological properties of the blood vessels passing through these points are utilized to identify these points as the vessel bifurcations and crossovers. We evaluate our method against manually measured bifurcation and crossover points by an expert, and achieved the detection accuracy of 95.82%.