Robust Vessel Segmentation Based on Multi-resolution Fuzzy Clustering

  • Gang Yu
  • Pan Lin
  • Shengzhen Cai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5326)


A novel multi-resolution approach is presented for vessel segmentation using multi-scale fuzzy clustering and vessel enhancement filtering. According to geometric shape analysis of the vessel structure with different scale, a new fuzzy inter-scale constraint based on antistrophic diffusion linkage model is introduced which builds an efficient linkage relationship between the high resolution feature images and low resolution ones. Meanwhile, this paper develops two new fuzzy distances which describe the fuzzy similarity of line-like structure in adjacent scales effectively. Moreover, a new multiresolution framework combining the inter- and intra-scale constraints is presented. The proposed framework is robust to noisy vessel images and low contrast ones, such as medical images. Segmentation of a number of vessel images shows that the proposed approach is robust and accurate.


Segmentation Result Fuzzy Cluster Vessel Segmentation Vessel Image Fuzzy Similarity 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M., Goldbaum, M.: Detection of blood vessels in retinal images using two dimensional matched filters. IEEE Trans. on Medical Imaging 8(3), 263–269 (1989)CrossRefGoogle Scholar
  2. 2.
    Thackray, B.D., Nelson, A.C.: Semiautomatic segmentation of vascular network images using a rotating structuring element (ROSE) with mathematical morphology anddual feature thresholding. IEEE Trans. On Medical Imaging 12(3), 385–392 (1993)CrossRefGoogle Scholar
  3. 3.
    Koen, L.V.: Probabilistic Multiscale Image Segmentation. IEEE Trans. Pattern Analysis and Machine Intelligence 19(2), 109–120 (1997)CrossRefGoogle Scholar
  4. 4.
    Sokratis, M.: Segmentation of Color Images Using Multiscale Clustering and Graph Theoretic Region Synthesis. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 35(2), 224–238 (2005)CrossRefGoogle Scholar
  5. 5.
    Nikos, P.: Geodesic active regions: A new framework to deal with frame partition problems in computer vision. Journal of Visual Communication and Image Representation 13, 249–268 (2002)CrossRefGoogle Scholar
  6. 6.
    Yezzi Jr., A., Andy, T., Alan, W.: A Fully Global Approach to Image Segmentation via Coupled Curve Evolution Equations. Journal of Visual Communication and Image Representation 13, 195–216 (2002)CrossRefGoogle Scholar
  7. 7.
    Pascal, M., Philippe, R., Francois, G., Prederic, G.: Influence of the Noise Model on Level Set Active Contour Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(6), 799–803 (2004)CrossRefGoogle Scholar
  8. 8.
    Ali, G., Raphael, C.: A new fast level set method. In: Proc. of the 6th Signal Processing Symposium, pp. 9–11 (2004)Google Scholar
  9. 9.
    Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. LNCS, vol. 1946, pp. 130–137. Springer, Heidelberg (1998)Google Scholar
  10. 10.
    Perona, P., Malik, J.: Scale-space and Edge Detection using Anisotropic Diffusion. IEEE Transaction On Pattern Anal. and Mach. Intell 12(6), 629–639 (1990)CrossRefGoogle Scholar
  11. 11.
    Yu, G.: A Novel Fuzzy Segmentation Approach for Brain MRI. In: Fischer, K., Timm, I.J., André, E., Zhong, N. (eds.) MATES 2006. LNCS (LNAI), vol. 4196, pp. 887–896. Springer, Heidelberg (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Gang Yu
    • 1
  • Pan Lin
    • 2
  • Shengzhen Cai
    • 2
  1. 1.School of Info-Physics and Geometics EngineeringCentral South UniversityHunanChina
  2. 2.Faculty of SoftwareFujian Normal UniversityFujianChina

Personalised recommendations