Segmentation of Prostate Using Interactive Finsler Active Contours and Shape Prior

  • Foued Derraz
  • Abdelmalik Taleb-Ahmed
  • Azzeddine Chikh
  • Christina Boydev
  • Laurent Peyrodie
  • Gerard Forzy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7340)


We present a new interactive segmentation framework to segment the prostate from MR prostate imagery. We first explicitly address the segmentation problem based on fast globally Finsler Active Contours (FAC) by incorporating both statistical and geometric shape prior knowledge. In doing so, we are able to exploit the more global aspects of segmentation by incorporating user feedback in segmentation process. In addition, once the prostate shape has been segmented, a cost functional is designed to incorporate both the local image statistics as user feedback and the learned shape prior. We provide experimental results, which include several challenging clinical data sets, to highlight the algorithm’s capability of robustly handling supine/prone prostate segmentation task.


Finsler Active contours characteristic function shape prior user interaction 


  1. 1.
    Zwiggelaar, R., Zhu, Y., Williams, S.: Semi-automatic Segmentation of the Prostate. In: Perales, F.J., Campilho, A.C., Pérez, N., Sanfeliu, A. (eds.) IbPRIA 2003. LNCS, vol. 2652, pp. 1108–1116. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  2. 2.
    Villeirs, G., De Meerleer, G.: Magnetic resonance imaging (MRI) anatomy of the prostate and application of MRI in radiotherapy planning. Eur. J. Radiol. 63(3), 361–368 (2007)CrossRefGoogle Scholar
  3. 3.
    Bresson, X., Esedoglu, S., Vandergheynst, P., Thiran, J., Osher, S.: Fast Global Minimization of the Active Contour/Snake Model. JMIV 28(2) (2007)Google Scholar
  4. 4.
    Michailovich, O., Rathi, Y., Tannenbaum, A.: Image Segmentation Using Active Contours Driven by the Bhattacharyya Gradient Flow. IEEE Trans. IP 16(11), 2787–2801 (2007)MathSciNetGoogle Scholar
  5. 5.
    Melonakos, J., Pichon, E., Angenent, S., Tannenbaum, A.: Finsler active contours. IEEE Trans. PAMI 30(3), 412–423 (2008)CrossRefGoogle Scholar
  6. 6.
    Foulonneau, A., Charbonnier, P., Heitz, F.: Affine-Invariant Geometric Shape Priors for Region-Based Active Contours. IEEE Trans. PAMI 28(8), 1352–1357 (2006)CrossRefGoogle Scholar
  7. 7.
    Pasquier, D., Lacornerie, T., Vermandel, M., Rousseau, J., Lartigau, E., Betrouni, N.: Automatic Segmentation of Pelvic Structures From Magnetic Resonance Images for Prostate Cancer Radiotherapy. Int. Jnl. of Radiation Oncology, Biology, Physics 68(2), 592–600 (2007)CrossRefGoogle Scholar
  8. 8.
    Mahdavi, S., Chng, N., Spadinger, I., Morris, W.J., Salcudean, S.E.: Semi-automatic segmentation for prostate interventions. Medical Image Analysis 15(2), 226–237 (2011)CrossRefGoogle Scholar
  9. 9.
    Derraz, F.: Optimal segmentation by fast binary geometric active contours, PhD Thesis (2010)Google Scholar
  10. 10.
    Duay, V., Houhou, N., Thiran, J.P.: Atlas-based segmentation of medical images locally constrained by level sets. In: IEEE ICIP 2005, vol. 2, pp. 1286–1289 (2005)Google Scholar
  11. 11.
    Martin, S., Daanen, V., Troccaz, J.: Atlas-based prostate segmentation using an hybrid registration. Int. J. CARS 3, 485–492 (2008)CrossRefGoogle Scholar
  12. 12.
    Aubert, G., Barlaud, M., Faugeras, O., Jehan-Besson, S.: Image segmentation using active contours: Calculus of variations or shape gradients? SIAM Applied Mathematics 63 (2002)Google Scholar
  13. 13.
    Klein, S., Staring, M., Pluim, J.P.W.: Evaluation of optimization methods for nonrigid medical image registration using mutual information and B-splines. IEEE Trans. Image Process. 16(12), 2879–2890 (2007)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Pasquier, D., Peyrodie, L., Denis, F., Pointreau, Y., Bera, G., Lartigau, E.: Segmentation automatique des images pour la planification dosimetrique en radiotherapie. Cancer/Radiotherapie 14(S.1), 6–13 (2010)CrossRefGoogle Scholar
  15. 15.
    Vikal, S., Haker, S., Tempany, C., Fichtinger, G.: Prostate contouring in MRI guided biopsy. In: SPIE Conf., vol. 7259, p. 144 (2009)Google Scholar
  16. 16.
    Pasquier, D., Lacornerie, T., Vermandel, M., Rousseau, J., Lartigau, E., Betrouni, N.: Automatic segmentation of pelvic structures from magnetic resonance images for prostate cancer radiotherapy. Int. J. Radiat. Oncol., Biol., Phys. 68(2), 592–600 (2007)CrossRefGoogle Scholar
  17. 17.
    Liu, X., Langer, D.L., Haider, M.A., Van der Kwast, T.H., Evans, A.J., Wernick, M.N., Yetik, I.S.: Unsupervised Segmentation of the Prostate Using MR Images Based on Level Set with a Shape Prior. In: IEEE EMBC 2009 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Foued Derraz
    • 1
    • 3
  • Abdelmalik Taleb-Ahmed
    • 3
  • Azzeddine Chikh
    • 4
  • Christina Boydev
    • 3
  • Laurent Peyrodie
    • 2
  • Gerard Forzy
    • 1
  1. 1.Faculté Libre de MédicineInstitut Catholique de LilleFrance
  2. 2.HEI, LAGIS UMR CNRS 3304LilleFrance
  3. 3.LAMIH UMR CNRS 8201ValenciennesFrance
  4. 4.Biomedical Engineering Laboratory, Technology CollegeAbou Bekr Belkaid UniversityAlgeria

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