Statistical shape analysis of multi-object complexes

Kevin Gorczowski, Martin Styner, Ja Yeon Jeong, J. S. Marron, Joseph Piven, Heather Cody Hazlett, Stephen M. Pizer, Guido Gerig

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

    Abstract

    An important goal of statistical shape analysis is the discrimination between populations of objects, exploring group differences in morphology not explained by standard volumetric analysis. Certain applications additionally require analysis of objects in their embedding context by joint statistical analysis of sets of interrelated objects. In this paper, we present a framework for discriminant analysis of populations of 3-D multi-object sets. In view of the driving medical applications, a skeletal object parametrization of shape is chosen since it naturally encodes thickening, bending and twisting. In a multi-object setting, we not only consider a joint analysis of sets of shapes but also must take into account differences in pose. Statistics on features of medial descriptions and pose parameters, which include rotational frames and distances, uses a Riemannian symmetric space instead of the standard Euclidean metric. Our choice of discriminant method is the distance weighted discriminant (DWD) because of its generalization ability in high dimensional, low sample size settings. Joint analysis of 10 subcortical brain structures in a pediatric autism study demonstrates that multi-object analysis of shape results in a better group discrimination than pose, and that the combination of pose and shape performs better than shape alone. Finally, given a discriminating axis of shape and pose, we can visualize the differences between the populations.

    Original languageEnglish (US)
    Title of host publication2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
    DOIs
    StatePublished - 2007
    Event2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 - Minneapolis, MN, United States
    Duration: Jun 17 2007Jun 22 2007

    Publication series

    NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
    ISSN (Print)1063-6919

    Other

    Other2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
    CountryUnited States
    CityMinneapolis, MN
    Period6/17/076/22/07

    ASJC Scopus subject areas

    • Software
    • Computer Vision and Pattern Recognition

    Fingerprint Dive into the research topics of 'Statistical shape analysis of multi-object complexes'. Together they form a unique fingerprint.

    Cite this