Author/Authors :
Bevilacqua, Vitoantonio Politecnico di Bari - Department of Electrotechnics and Electronics, Italy , Bevilacqua, Vitoantonio Spin-Off of Polytechnic of Bari - e B I S s r l (electronic Business in Security), Italy , Cariello, Lucia Polytechnic of Bari - Dept of Electrotechnics and Electronics, Italy , Cariello, Lucia Spin-Off of Polytechnic of Bari - eBIS s r l (electronic Business In Security), Italy , Giannini, Marco Spin-Off of Polytechnic of Bari - eBIS s r l (electronic Business In Security), Italy , Giannini, Marco Polytechnic of Bari - Dept of Electrotechnics and Electronics, Italy , Mastronardi, Giuseppe Spin-Off of Polytechnic of Bari - e B I S s r l (electronic Business in Security), Italy , Mastronardi, Giuseppe Politecnico di Bari - Department of Electrotechnics and Electronics, Italy , Santarcangelo, Vito Spin-Off of Polytechnic of Bari - eBIS s r l (electronic Business In Security), Italy , Santarcangelo, Vito Polytechnic of Bari - Dept of Electrotechnics and Electronics, Italy , Scaramuzzi, Rocco Polytechnic of Bari - Dept of Electrotechnics and Electronics, Italy , Troccoli, Antonella Polytechnic of Bari - Dept of Electrotechnics and Electronics, Italy
Abstract :
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
Keywords :
personal identification , retinal fundus , blood vessels detection , preprocessing , gaussian derivation , crossover points extraction , blood vessels skeleton , ANN
