The blood vessel recognition of ocular fundus

Today, iris recognition, fingerprint recognition, face recognition, voice recognition and other biometric technology are experiencing rapid development. This paper addresses a new biometric technology - skeleton recognition for blood vessel of optic fundus. The green grayscale ocular fundus image is utilized. The skeleton feature of blood vessel of optic fundus is extracted at first. After filtering treatment and extracting feature, vector curve of blood vessels is obtained. Shape curve matching is later carried out by means of reference point matching. The recognition for blood vessel optic fundus has been demonstrated in this paper to possess high recognition rate, low recognition rejection rate as well as good universality, exclusiveness and stability. With more and more progress made in extracting technology, the optic fundus blood vessel recognition is to become an effective biometric technology.

[1]  Gérard G. Medioni,et al.  Refining edges detected by a LoG operator , 1990, Comput. Vis. Graph. Image Process..

[2]  M. Voutay,et al.  Morphological Filters And Edge Detection Application To Medical Imaging , 1991, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society Volume 13: 1991.

[3]  Guy Marchal,et al.  Multi-modality image registration by maximization of mutual information , 1996, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis.

[4]  Faculte De Medecine.  MORPHOLOGICAL FILTERS AND EDGE DETECTION APPLICATION TO MEDICAL IMAGING J. PENG, Ph. RUSCH, T. HERRMANN, P.P. LEVY M. VOUTAY, A. ELBOUSTINI, J.C. HEALY , 1991 .

[5]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  C. C. Li,et al.  A Fast Morphological Filter For Enhancement Of Angiographic Images , 1991, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society Volume 13: 1991.

[7]  Konstantina S. Nikita,et al.  Automatic retinal image registration scheme using global optimization techniques , 1999, IEEE Transactions on Information Technology in Biomedicine.

[8]  Primo Zingaretti,et al.  Retina vascular network recognition , 1993 .

[9]  M. Goldbaum,et al.  Detection of blood vessels in retinal images using two-dimensional matched filters. , 1989, IEEE transactions on medical imaging.

[10]  Malur K. Sundareshan,et al.  Adaptive image contrast enhancement based on human visual properties , 1994, IEEE Trans. Medical Imaging.

[11]  Guy Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.