A Shape-Based Approach to Robust Image Segmentation

  • Samuel Dambreville
  • Yogesh Rathi
  • Allen Tannenbaum
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4141)


We propose a novel segmentation approach for introducing shape priors in the geometric active contour framework. Following the work of Leventon, we propose to revisit the use of linear principal component analysis (PCA) to introduce prior knowledge about shapes in a more robust manner. Our contribution in this paper is twofold. First, we demonstrate that building a space of familiar shapes by applying PCA on binary images (instead of signed distance functions) enables one to constrain the contour evolution in a way that is more faithful to the elements of a training set. Secondly, we present a novel region-based segmentation framework, able to separate regions of different intensities in an image. Shape knowledge and image information are encoded into two energy functionals entirely described in terms of shapes. This consistent description allows for the simultaneous encoding of multiple types of shapes and leads to promising segmentation results. In particular, our shape-driven segmentation technique offers a convincing level of robustness with respect to noise, clutter, partial occlusions, and blurring.


Segmentation Result Active Contour Shape Space Kernel Principal Component Analysis Initial Contour 


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  1. 1.
    Leventon, M., Grimson, E., Faugeras, O.: Statistical shape influence in geodesic active contours. In: Proc. CVPR, pp. 1316–1324. IEEE, Los Alamitos (2000)Google Scholar
  2. 2.
    Paragios, N., Deriche, R.: Geodesic active contours and level sets for the detection and tracking of moving objects. Transactions on Pattern analysis and Machine Intelligence 22, 266–280 (2000)CrossRefGoogle Scholar
  3. 3.
    Tsai, A., Yezzi, T., Wells, W., et al.: A shape-based approach to the segmentation of medical imagery using level sets. IEEE Trans. on Medical Imaging 22, 137–153 (2003)CrossRefGoogle Scholar
  4. 4.
    Yezzi, A., Kichenassamy, S., Kumar, A., et al.: A geometric snake model for segmentation of medical imagery. IEEE Trans. Medical Imag. 16, 199–209 (1997)CrossRefGoogle Scholar
  5. 5.
    Yezzi, A., Soatto, S.: Deformotion: Deforming motion, shape average and the joint registration and approximation of structures in images. International Journal of Computer Vision 53, 153–167 (2003)CrossRefGoogle Scholar
  6. 6.
    Blake, A., Isard, M. (eds.): Active Contours. Springer, Heidelberg (1998)Google Scholar
  7. 7.
    Cootes, T., Taylor, C., Cooper, D., et al.: Active shape models-their training and application. Comput. Vis. Image Understanding 61, 38–59 (1995)CrossRefGoogle Scholar
  8. 8.
    Wang, Y., Staib, L.: Boundary finding with correspondance using statistical shape models. In: IEEE Conf. Computer Vision and Pattern Recognition, pp. 338–345 (1998)Google Scholar
  9. 9.
    Cremers, D., Kohlberger, T., Schnoerr, C.: Diffusion snakes: introducing statistical shape knowledge into the mumford-shah functional. International journal of computer vision 50 (2002)Google Scholar
  10. 10.
    Cremers, D., Kohlberger, T., Schnoerr, C.: Shape statistics in kernel space for variational image segmentation. Pattern Recognition 36, 1292–1943 (2003)CrossRefGoogle Scholar
  11. 11.
    Mika, S., Scholkopf, B., Smola, A., et al.: Kernel pca and de-noising in feature spaces. In: Advances in neural information processing systems, vol. 11 (1998)Google Scholar
  12. 12.
    Sapiro, G.: Geometric Partial Differential Equations and Image Analysis. Cambridge University Press, Cambridge (2001)CrossRefMATHGoogle Scholar
  13. 13.
    Osher, S., Sethian, J.: Fronts propagation with curvature dependent speed: Algorithms based on hamilton-jacobi formulations. Journal of Computational Physics 79, 12–49 (1988)CrossRefMathSciNetMATHGoogle Scholar
  14. 14.
    Rousson, M., Paragios, N.: Shape priors for level set representations. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 78–92. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  15. 15.
    Yezzi, A., Tsai, A., Willsky, A.: A statistical approach to snakes for bimodal and trimodal imagery. In: Proc. Int. Conf. Computer Vision, vol. 2, pp. 898–903 (1999)Google Scholar
  16. 16.
    Chan, T., Vese, L.: Active contours without edges. IEEE Trans. Image Processing 10, 266–277 (2001)CrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Samuel Dambreville
    • 1
  • Yogesh Rathi
    • 1
  • Allen Tannenbaum
    • 1
  1. 1.Georgia Institute of TechnologyAtlanta GeorgiaUSA

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