title: A study on graphical model structure for representing statistical shape model of point distribution model.
abstract: In this article, the authors demonstrate that you can improve the performance of the registration of a point distribution model (PDM) by accurately estimating the structure of an undirected graphical model that represents the statistical shape model (SSM) of a target surface. Many existing methods for constructing SSMs determine the structure of the graphical model without analyzing the conditional dependencies among the points in PDM, though an edge in the PDM should link two nodes if and only if they are conditionally dependent. In this study, the authors employed four popular methods for estimating the structure of graphical model and obtained four different SSMs from an identical set of training surfaces. The registration performances of the SSMs were experimentally compared, and the results showed that the graphical lasso, which could estimate more accurate structure of the graphical model by avoiding the overfitting to the training data, outperformed the other methods.
authors.affiliation: Nagoya Institute of Technology, Aichi, Japan.
authors.forename: Yoshihide
authors.initials: Y
authors.lastname: Sawada
authors.forename: Hidekata
authors.initials: H
authors.lastname: Hontani
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016008:Statistical Distributions; D014057:Tomography, X-Ray Computed / Q000379:methods*