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Recognition for Ocular Fundus Based on Shape of Blood Vessel

  • Zhiwen Xu
  • Xiaoxin Guo
  • Xiaoying Hu
  • Xu Chen
  • Zhengxuan Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3926)

Abstract

A new biometric technology-recognition ocular fundus based on shape of blood vessel skeleton-is addressed in this paper. The gray scale image of ocular fundus is utilized to extract the skeletons of its blood vessels. The cross points on the skeletons are used to match two fundus images. Experiments show high recognition rate, low recognition rejection rate as well as good universality, exclusiveness and stability of this method.

Keywords

Feature Point Gray Scale Image Vector Curve Fundus Image Template Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Ulupinar, F., Medioni, G.: Refining edges detected by LoG operator. Computer Vis Graph and Image Process 51, 275–298 (1990)CrossRefGoogle Scholar
  2. 2.
    Canny, J.: Acomputational approach to edge detection. IEEE Trans, PAMI 8, 679–698 (1986)CrossRefGoogle Scholar
  3. 3.
    Peng, J., Rusch, P.: Morphological filters and edge detection application to medical imaging. In: Annual International Conference of the IEEE Engineering In Medicine and Biology Society, vol. 13(1), pp. 251–252 (1991)Google Scholar
  4. 4.
    Huang, C.C., Li, C.C., Fan, N., et al.: A fast morphological filter for enhancement of angiographic images. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 13(1), pp. 229–230 (1991)Google Scholar
  5. 5.
    Tascini, G., Passerini, G., Puliti, P., et al.: Retina vascular network recognition. Proc. SPIE 1898, 322–329 (1993)CrossRefGoogle Scholar
  6. 6.
    Chauduri, S., Chatterjee, S., Katz, N., et al.: Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans. Med. Imaging 8, 263–269 (1989)CrossRefGoogle Scholar
  7. 7.
    Ji, T.-L., Sundareshan, M.K., Roehrig, H.: Adaptive image contrast enhancement based on human visual properties. IEEE Trans. Med. Imaging 13, 573–586 (1994)CrossRefGoogle Scholar
  8. 8.
    Matsopoulos, G.K., Mouravliansky, N.A., Delibasis, K.K., et al.: Automatic retinal image registration scheme using global optimization techniques. IEEE Trans. Information Technology in Biomedicine 3(1), 47–60 (1999)CrossRefGoogle Scholar
  9. 9.
    Maes, F., Collignon, A., Vandermeulen, D., et al.: Multi-modality image registration by maximization of mutual information. IEEE Trans. Med. Img. 16(2), 187–198 (1997)CrossRefGoogle Scholar
  10. 10.
    Zhan, X.-S., Ning, X.-B., Yin, Y.-L., Chen, Y.: An improved point pattern algorithm for fingerprint matching. Journal of Nanjing University 39(4), 491–498 (2003)Google Scholar
  11. 11.
    Qi, Y., Tian, J., Deng, X.: Genetic algorithm based fingerprint matching algorithm and its application on automated fingerprint identification system. Journal of Software 11(4), 488–493 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zhiwen Xu
    • 1
    • 2
  • Xiaoxin Guo
    • 1
    • 2
  • Xiaoying Hu
    • 3
  • Xu Chen
    • 1
    • 2
  • Zhengxuan Wang
    • 1
    • 2
  1. 1.Key Laboratory of Symbolic Computation and Knowledge EngineeringJilin UniversityChangchunChina
  2. 2.College of Computer Science and TechnologyJilin UniversityChangchunChina
  3. 3.The First Clinical HospitalJilin UniversityChangchunChina

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