Abstract
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).
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
Felson, D.: Clinical Practice. Osteoarthritis of the Knee. N. Engl. J. Med. 354, 841–848 (2006)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Wang, Y., Staib, L.: Physical model-based non-rigid registration incorporating statistical shape information. Med. Image Anal. 4(1), 7–20 (2000)
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)
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)
Geman, S., Geman, D.: Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6, 721–741 (1984)
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)
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)
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)
Delingette, H.: General object reconstruction based on simplex meshes. Int. J. Comput. Vis. 32(2), 111–146 (1999)
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)
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)
Author information
Authors and Affiliations
Editor information
Electronic Supplementary Material
Supplementary Material (4,090 KB)
978-3-540-85988-8_15_MOESM2_ESM.mpg
Supplementary Material (5,836 KB)
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Schmid, J., Magnenat-Thalmann, N. (2008). MRI Bone Segmentation Using Deformable Models and Shape Priors. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008. MICCAI 2008. Lecture Notes in Computer Science, vol 5241. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85988-8_15
Download citation
DOI: https://doi.org/10.1007/978-3-540-85988-8_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85987-1
Online ISBN: 978-3-540-85988-8
eBook Packages: Computer ScienceComputer Science (R0)