A Comparison Between a Geometrical and an ANN Based Method for Retinal Bifurcation Points Extraction
This paper describes a comparative study between an Artificial Neural Network (ANN) and a geometric technique to detect for biometric applications, the bifurcation points of blood vessels in the retinal fundus. The first step is an image preprocessing phase to extract retina blood vessels. The contrast of the blood vessels from the retinal image background is enhanced in order to extract the blood vessels skeleton. Successively, candidate points of bifurcation are individualized by approximating the skeleton lines in segments. The distinction between bifurcations and vessel bends is carried out through the employment of two methods: geometric (through the study of intersections within the region obtained thresholding the image portion inside a circle centered around the junctions point and the circumference of the same circle) and an ANN. The results obtained are compared and discussed.
ann retinal fundus preprocessing blood vessels skeleton personal identification blood vessels detection gaussian derivation crossover points extraction