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Inferring Vascular Structures in Coronary Artery X-Ray Angiograms Based on Multi-Feature Fuzzy Recognition Algorithm

  • Shoujun Zhou
  • Wufan Chen
  • Jiangui Zhang
  • Yongtian Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4091)

Abstract

The multi-feature fuzzy recognition (MFFR) algorithm was presented to infer the vessel structures, in the context of X-Ray Angiograms (XRA) of the coronary artery. In the modeling, a multi-feature metrics (MFM) was firstly established to describe the local configuration; then the membership degree of MFM-based fuzzy subsets was defined, and the fuzzy recognition operator was constructed. The MFFR algorithm can correctly infer four kinds of vessel structures including vascular ends, segments, bifurcations and crossovers. The results are satisfying: on average 91.1% of the testing vessel lengths in medium quality images are automatically delineated as well as their structures being correctly inferred with point-wise.

Keywords

Multi-feature fuzzy recognition vessel structure inference coronary artery X-ray angiograms 

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References

  1. 1.
    Shen, H., Stewart, C., Roysam, B.: Optimal scheduling of tracing computations for real-time vascular landmark extraction from retinal fundus images. IEEE Transactions on Information Technology in Biomedicine 5(1), 77–91 (2001)CrossRefGoogle Scholar
  2. 2.
    Schrijver, M., Slump, C.H.: Automatic segmentation of the coronary artery tree in angiographic projections. In: Proceedings of ProRISC 2002. November 28-29, Veldhoven, the Netherlands, (2002)Google Scholar
  3. 3.
    Bhalerao, Wilson, R.: Estimating Local and Global Structure using a Gaussian Intensity Model. In: Proceedings of Medical Image Understanding and Analysis (MIUA) (July 2001)Google Scholar
  4. 4.
    Tsai, C.-L., Stewart, C.V., Tanenbaum, H.L., Roysam, B.: Model-Based Method for Improving The Accuracy and Repeatability of Estimating Vascular Bifurcations and Crossovers From Retinal Fundus Images. IEEE Transactions on information technology in biomedicine 8(2), 122–130 (2004)CrossRefGoogle Scholar
  5. 5.
    Wang, L., Bhalerao, A., Wilson, R.: Robust Modelling of Local Image Structures and its Application to Medical Imagery. In: Proc. Int. Conf. Pattern Recognition, ICPR 2004, Cambridge (2004)Google Scholar
  6. 6.
    Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multi-scale vessel enhancement filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)Google Scholar
  7. 7.
    Sang, N., Tang, Q., Liu, X., Weng, W.: Multiscale Centerline Extraction of Angiogram Vessels Using Gabor Filters. In: Zhang, J., He, J.-H., Fu, Y. (eds.) CIS 2004. LNCS, vol. 3314, pp. 570–575. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shoujun Zhou
    • 1
  • Wufan Chen
    • 2
  • Jiangui Zhang
    • 2
  • Yongtian Wang
    • 1
  1. 1.School of Information Science and TechnologyBeijing Institute of TechnologyB.J.China
  2. 2.School of Biomedical EngineeringSouthern Medical UniversityG.Z.China

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