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MRI Bone Segmentation using Deformable Models and Shape Priors

Schmid, J. and Magnenat-Thalmann, N.


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).


@inproceedings{489,
  booktitle = {MICCAI '08, Part I. LNCS},
  author = {Schmid, J. and Magnenat-Thalmann, N.},
  title = {MRI Bone Segmentation using Deformable Models and Shape Priors},
  publisher = {Springer Berlin / Heidelberg},
  volume = {5241},
  pages = {119-126},
  month = sep,
  year = {2008},
  topic = {Medical}
}