A Novel Liver Perfusion Analysis Based on Active Contours and Chamfer Matching

  • Gang Chen
  • Lixu Gu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4091)


Liver Perfusion gives important information about blood supply of liver. However, in daily clinical diagnosis, radiologists have to manually mark the perfusion position in time-sequence images due to the motion of liver caused by respiration. In this paper, we propose a novel hybrid method using a variation of active contours and modified chamfer matching to automatically detect the liver perfusion position and measure the intensity with a single shape prior. The experiment is taken on abdomen MRI series and the result reveals that after extracting liver’s rough boundary by active contours, precise perfusion positions can be detected by the modified chamfer matching algorithm, and finally a refined intensity curve without respiration affection can be achieved.


Liver Region Source Image Segmentation Result Active Contour Liver Perfusion 
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  1. 1.
    Bader, T.R., Herneth, A.M., Blaicher, W., Steininger, R., Muhlbacher, F., Lechner, G., Grabenwoger, F.: Hepatic perfusion after liver transplantation: noninvasive measurement with dynamic single-section CT. Radiology 209, 129–134 (1998)Google Scholar
  2. 2.
    Sebastian, N., Gu, L.: A Novel Liver Perfusion Analysis Method. In: IEEE Engineering in Medicine and Biology 27th Annual Conference (2005)Google Scholar
  3. 3.
    Sethian, J.A.: A Fast Marching Level Set Method for Monotonically Advancing Fronts. Proceedings of the National Academy of Sciences 93(4), 1591–1595 (1996)MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Osher, S., Sethian, J.: Fronts Propagating with Curvature-Dependent Speed: Algorithms Based on Hamilton-Jacobi Formulations. Journal of Computation Physics 79, 12–49 (1998)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Malladi, R., Sethian, J.A.: An O(N log N) algorithm for shape modeling. Proceedings of the National Academy of Sciences 93, 9389–9392 (1996)MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Seniha, E.Y., Ayman, E., Aly, A., Mohamed, E., Tarek, A., Mohamed, A.: Automatic Detection of Renal Rejection after Kidney Transplantation. Computer Assisted Radiology and Surgery, 773–778 (2005)Google Scholar
  7. 7.
    Kass, M., Witkin, A., Terzopolous, D.: Snake: Active contour models. In: First International Conference on Computer Vision, pp. 259–268 (1987)Google Scholar
  8. 8.
    Gunnila, B.: Hierarchical chamfer matching: A parametric edge matching algorithm. IEEE transaction on Pattern Analysis and Machine Intelligence 10(6), 849–856 (1988)CrossRefGoogle Scholar
  9. 9.
    Barrow, H.G., Tenenbaum, J.M., Bolles, R.C., Wolf, H.C.: Parametric correspondence and chamfer matching: Two new techniques for image matching. In: Proc. 5th Int. Joint Conf. Artifical Intelligence, pp. 659–663 (1997)Google Scholar
  10. 10.
    Chan, T., Vese, L.: Active contours without edges. IEEE trans. Image Processing, 266–277 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Gang Chen
    • 1
  • Lixu Gu
    • 1
  1. 1.Computer ScienceShanghai Jiao Tong UniversityShanghaiP.R. China

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