Estimation of shape model parameters for 3D surfaces

Søren Gylling Hemmingsen Erbou, Sune Darkner, Jurgen Fripp, Sébastien Ourselin, Bjarne Kjær Ersbøll

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    Statistical shape models are widely used as a compact way of representing shape variation. Fitting a shape model to unseen data enables characterizing the data in terms of the model parameters. In this paper a Gauss-Newton optimization scheme is proposed to estimate shape model parameters of 3D surfaces using distance maps, which enables the estimation of model parameters without the requirement of point correspondence. For applications with acquisition limitations such as speed and cost, this formulation enables the fitting of a statistical shape model to arbitrarily sampled data. The method is applied to a database of 3D surfaces from a section of the porcine pelvic bone extracted from 33 CT scans. A leave-one-out validation shows that the parameters of the first 3 modes of the shape model can be predicted with a mean difference within [-0.01,0.02] from the true mean, with a standard deviation less than 0.34.
    Original languageEnglish
    Title of host publication5th IEEE International Symposium on Biomedical Imaging
    Publication date2008
    ISBN (Print)14-24-42002-4
    Publication statusPublished - 2008
    Event2008 IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Paris, France
    Duration: 14 May 200817 May 2008
    Conference number: 5


    Conference2008 IEEE International Symposium on Biomedical Imaging
    Internet address

    Bibliographical note

    Copyright: 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE


    • Optimization methods
    • X-ray tomography
    • Image shape analysis
    • Image registration
    • Biomedical image processing


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