title: Hypothesis testing with nonlinear shape models.
abstract: We present a method for two-sample hypothesis testing for statistical shape analysis using nonlinear shape models. Our approach uses a true multivariate permutation test that is invariant to the scale of different model parameters and that explicitly accounts for the dependencies between variables. We apply our method to m-rep models of the lateral ventricles to examine the amount of shape variability in twins with different degrees of genetic similarity.
authors.affiliation: Dept. of Computer Science, Univ. of North Carolina, Chapel Hill, NC 27599, USA. tterribe@cs.unc.edu
authors.forename: Timothy B
authors.initials: TB
authors.lastname: Terriberry
authors.forename: Sarang C
authors.initials: SC
authors.lastname: Joshi
authors.forename: Guido
authors.initials: G
authors.lastname: Gerig
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002552:Cerebral Ventricles / Q000473:pathology*; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012559:Schizophrenia / Q000473:pathology*; D012680:Sensitivity and Specificity; D014430:Twins, Monozygotic