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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    Abstract

    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
    PublisherSpringer
    Publication date2002
    Publication statusPublished - 2002

    Cite this

    Larsen, R., Hilger, K. B., & Wrobel, M. C. (2002). Statistical 2D and 3D shape analysis using Non-Euclidean Metrics. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2002, 5th Int. Conference, Tokyo, Japan Springer.
    Larsen, Rasmus ; Hilger, Klaus Baggesen ; Wrobel, Mark Christoph. / Statistical 2D and 3D shape analysis using Non-Euclidean Metrics. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2002, 5th Int. Conference, Tokyo, Japan. Springer, 2002.
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    abstract = "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.",
    keywords = "maximum noise fractions, shape analysis, growth modelling, maximum autocorrelation factors",
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    Larsen, R, Hilger, KB & Wrobel, MC 2002, Statistical 2D and 3D shape analysis using Non-Euclidean Metrics. in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2002, 5th Int. Conference, Tokyo, Japan. Springer.

    Statistical 2D and 3D shape analysis using Non-Euclidean Metrics. / Larsen, Rasmus; Hilger, Klaus Baggesen; Wrobel, Mark Christoph.

    Medical Image Computing and Computer-Assisted Intervention - MICCAI 2002, 5th Int. Conference, Tokyo, Japan. Springer, 2002.

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

    TY - GEN

    T1 - Statistical 2D and 3D shape analysis using Non-Euclidean Metrics

    AU - Larsen, Rasmus

    AU - Hilger, Klaus Baggesen

    AU - Wrobel, Mark Christoph

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    N2 - 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.

    AB - 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.

    KW - maximum noise fractions

    KW - shape analysis

    KW - growth modelling

    KW - maximum autocorrelation factors

    M3 - Article in proceedings

    BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2002, 5th Int. Conference, Tokyo, Japan

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    Larsen R, Hilger KB, Wrobel MC. Statistical 2D and 3D shape analysis using Non-Euclidean Metrics. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2002, 5th Int. Conference, Tokyo, Japan. Springer. 2002