MRI Bone Segmentation Using Deformable Models and Shape Priors

  • Jérôme Schmid
  • Nadia Magnenat-Thalmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5241)


This paper addresses the problem of automatically segmenting bone structures in low resolution clinical MRI datasets. The novel aspect of the proposed method is the combination of physically-based deformable models with shape priors. Models evolve under the influence of forces that exploit image information and prior knowledge on shape variations. The prior defines a Principal Component Analysis (PCA) of global shape variations and a Markov Random Field (MRF) of local deformations, imposing spatial restrictions in shapes evolution. For a better efficiency, various levels of details are considered and the differential equations system is solved by a fast implicit integration scheme. The result is an automatic multilevel segmentation procedure effective with low resolution images. Experiments on femur and hip bones segmentation from clinical MRI depict a promising approach (mean accuracy: 1.44±1.1 mm, computation time: 2mn43s).


Principal Component Analy Active Contour Markov Random Fields Deformable Model Bone Segmentation 
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.

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  1. 1.
    Felson, D.: Clinical Practice. Osteoarthritis of the Knee. N. Engl. J. Med. 354, 841–848 (2006)Google Scholar
  2. 2.
    Pfirrmann, C.W.A., Mengiardi, B., Dora, C., Kalberer, F., Zanetti, M., Hodler, J.: Cam and Pincer Femoroacetabular Impingement: Characteristic MR Arthrographic Findings in 50 Patients. Radiology 240(3), 778–784 (2006)CrossRefGoogle Scholar
  3. 3.
    Fripp, J., Crozier, S., Warfield, S., Ourselin, S.: Automatic Segmentation of the Bone and Extraction of the Bone-cartilage Interface form Magnetic Resonance Images of the Knee. Phys. Med. Biol. 52, 1617–1631 (2007)CrossRefGoogle Scholar
  4. 4.
    Gilles, B., Moccozet, L., Magnenat-Thalmann, N.: Anatomical modelling of the musculoskeletal system from MRI. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 289–296. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    Lorigo, L.M., Faugeras, O.D., Grimson, W.E.L., Keriven, R., Kikinis, R.: Segmentation of Bone in Clinical Knee MRI using Texture-based Geodesic Active Contours. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 1195–1204. Springer, Heidelberg (1998)Google Scholar
  6. 6.
    Yang, J., Duncan, J.S.: 3d image segmentation of deformable objects with joint shape-intensity prior models using level sets. Med. Image Anal. 8, 285–294 (2004)CrossRefGoogle Scholar
  7. 7.
    Leventon, M.E., Grimson, W.E.L., Faugeras, O.: Statistical shape influence in geodesic active contours. In: Proc. IEEE Conf. Comput. Vis. Pattern Recogn., vol. 1, pp. 316–323 (2000)Google Scholar
  8. 8.
    Lamecker, H., Seebaß, M., Hege, H.C., Deuflhard, P.: A 3d statistical shape model of the pelvic bone for segmentation. In: Proc. of the SPIE, vol. 5370, pp. 1341–1351 (2004)Google Scholar
  9. 9.
    Dong, X., Gonzalez Ballester, M.A., Zheng, G.: Automatic extraction of femur contours from calibrated x-ray images using statistical information. J. Multimed. 2(5), 46–54 (2007)Google Scholar
  10. 10.
    Costa, M., Delingette, H., Novellas, S., Ayache, N.: Automatic segmentation of bladder and prostate using coupled 3d deformable models. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 252–260. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Wang, Y., Staib, L.: Physical model-based non-rigid registration incorporating statistical shape information. Med. Image Anal. 4(1), 7–20 (2000)CrossRefGoogle Scholar
  12. 12.
    Kervrann, C., Heitz, F.: A hierarchical markov modeling approach for the segmentation and tracking of deformable shapes. Graph. Model. Image Process 60(3), 173–195 (1998)CrossRefGoogle Scholar
  13. 13.
    Huang, R., Pavlovic, V., Metaxas, D.N.: A graphical model framework for coupling mrfs and deformable models. In: Proc. Conf. Comput. Vis. Pattern Recogn (CVPR 2004), vol. 02, pp. 739–746 (2004)Google Scholar
  14. 14.
    Geman, S., Geman, D.: Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6, 721–741 (1984)zbMATHCrossRefGoogle Scholar
  15. 15.
    Martín-Fernández, M., Alberola-López, C.: An approach for contour detection of human kidneys from ultrasound images using markov random fields and active contours. Med. Image Anal. 9(1), 1–23 (2005)CrossRefGoogle Scholar
  16. 16.
    Volino, P., Magnenat-Thalmann, N.: Implementing fast cloth simulation with collision response. In: Proc. Int. Conf. on Computer Graphics (CGI 2000), pp. 257–266. IEEE Computer Society, Los Alamitos (2000)CrossRefGoogle Scholar
  17. 17.
    Nealen, A., Müller, M., Keiser, R., Boxerman, E., Carlson, M.: Physically based deformable models in computer graphics. Computer Graphics Forum 25(4), 809–836 (2006)CrossRefGoogle Scholar
  18. 18.
    Delingette, H.: General object reconstruction based on simplex meshes. Int. J. Comput. Vis. 32(2), 111–146 (1999)CrossRefGoogle Scholar
  19. 19.
    Cootes, T.F., Hill, A., Taylor, C.J., Haslam, J.: The use of active shape models for locating structures in medical images. In: Barrett, H.H., Gmitro, A.F. (eds.) IPMI 1993. LNCS, vol. 687, pp. 33–47. Springer, Heidelberg (1993)CrossRefGoogle Scholar
  20. 20.
    Davies, R.H., Twining, C.J., Cootes, T.F., Waterton, J.C., Taylor, C.J.: 3d statistical shape models using direct optimisation of description length. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 3–20. Springer, Heidelberg (2002)CrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Jérôme Schmid
    • 1
  • Nadia Magnenat-Thalmann
    • 1
  1. 1.MIRALabUniversity of GenevaGenevaSwitzerland

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