title: 3D+t brain MRI segmentation using robust 4D Hidden Markov Chain.
abstract: In recent years many automatic methods have been developed to help physicians diagnose brain disorders, but the problem remains complex. In this paper we propose a method to segment brain structures on two 3D multi-modal MR images taken at different times (longitudinal acquisition). A bias field correction is performed with an adaptation of the Hidden Markov Chain (HMC) allowing us to take into account the temporal correlation in addition to spatial neighbourhood information. To improve the robustness of the segmentation of the principal brain structures and to detect Multiple Sclerosis Lesions as outliers the Trimmed Likelihood Estimator (TLE) is used during the process. The method is validated on 3D+t brain MR images.
authors.forename: François
authors.initials: F
authors.lastname: Lavigne
authors.forename: Christophe
authors.initials: C
authors.lastname: Collet
authors.forename: Jean-Paul
authors.initials: JP
authors.lastname: Armspach
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D019992:Databases as Topic; D066128:Gray Matter / Q000473:pathology; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D020407:Internet; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008390:Markov Chains; D009103:Multiple Sclerosis / Q000175:diagnosis; D013997:Time Factors; D066127:White Matter / Q000473:pathology