2005 | OriginalPaper | Buchkapitel
Hypothesis Testing with Nonlinear Shape Models
verfasst von : Timothy B. Terriberry, Sarang C. Joshi, Guido Gerig
Erschienen in: Information Processing in Medical Imaging
Verlag: Springer Berlin Heidelberg
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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.