Statistical 2D and 3D shape analysis using Non-Euclidean Metrics

Rasmus Larsen, Klaus Baggesen Hilger, Mark Christoph Wrobel

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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    We address the problem of extracting meaningful, uncorrelated biological modes of variation from tangent space shape coordinates in 2D and 3D using non-Euclidean metrics. We adapt the maximum autocorrelation factor analysis and the minimum noise fraction transform to shape decomposition. Furthermore, we study metrics based on repated annotations of a training set. We define a way of assessing the correlation between landmarks contrary to landmark coordinates. Finally, we apply the proposed methods to a 2D data set consisting of outlines of lungs and a 3D/(4D) data set consisting of sets of mandible surfaces. In the latter case the end goal is to construct a model for growth prediction and simulation.
    Original languageEnglish
    Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2002, 5th Int. Conference, Tokyo, Japan
    Publication date2002
    Publication statusPublished - 2002


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