title: Hierarchical bayesian modeling, estimation, and sampling for multigroup shape analysis.
abstract: This paper proposes a novel method for the analysis of anatomical shapes present in biomedical image data. Motivated by the natural organization of population data into multiple groups, this paper presents a novel hierarchical generative statistical model on shapes. The proposed method represents shapes using pointsets and defines a joint distribution on the population's (i) shape variables and (ii) object-boundary data. The proposed method solves for optimal (i) point locations, (ii) correspondences, and (iii) model-parameter values as a single optimization problem. The optimization uses expectation maximization relying on a novel Markov-chain Monte-Carlo algorithm for sampling in Kendall shape space. Results on clinical brain images demonstrate advantages over the state of the art.
authors.forename: Yen-Yun
authors.initials: YY
authors.lastname: Yu
authors.forename: P Thomas
authors.initials: PT
authors.lastname: Fletcher
authors.forename: Suyash P
authors.initials: SP
authors.lastname: Awate
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000473:pathology*; D001499:Bayes Theorem; D003198:Computer Simulation; D006624:Hippocampus / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008953:Models, Anatomic; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity