Hypothesis testing with nonlinear shape models

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

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    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.

    Original languageEnglish (US)
    Title of host publicationLecture Notes in Computer Science
    EditorsG.E. Christensen, M. Sonka
    Pages15-26
    Number of pages12
    Volume3565
    StatePublished - 2005
    Event19th International Conference on Information Processing in Medical Imaging, IPMI 2005 - Glenwood Springs, CO, United States
    Duration: Jul 10 2005Jul 15 2005

    Other

    Other19th International Conference on Information Processing in Medical Imaging, IPMI 2005
    CountryUnited States
    CityGlenwood Springs, CO
    Period7/10/057/15/05

    ASJC Scopus subject areas

    • Computer Science (miscellaneous)

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