N.: Mri bone segmentation using deformable models and shape priors

Abstract. This paper addresses the problem of automatically segment-ing 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 differ-ential 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 segmen-tation from clinical MRI depict a promising approach (mean accuracy: 1.44 ± 1.1 mm, computation time: 2mn43s).