title: MRI bone segmentation using deformable models and shape priors.
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: 2 mn 43 s).
authors.affiliation: MIRALab, University of Geneva, CH-1211 Geneva, Switzerland. schmid@miralab.unige.ch
authors.forename: Jérome
authors.initials: J
authors.lastname: Schmid
authors.forename: Nadia
authors.initials: N
authors.lastname: Magnenat-Thalmann
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D004548:Elasticity; D005269:Femur / Q000033:anatomy & histology*; D006621:Hip Joint / Q000033:anatomy & histology*; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique