Hypothesis Testing with Nonlinear Shape Models

Timothy B. Terriberry, Sarang C. Joshi, Guido Gerig

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.