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Statistical 2D and 3D Shape Analysis Using Non-Euclidean Metrics

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Abstract

The contribution of this paper is the adaptation of data driven methods for non-Euclidean metric decomposition of tangent space shape coordinates. This basic idea is to take extend principal components analysis to take into account the noise variance at different landmarks and at different shapes. We show examples where these non-Euclidean metric methods allow for easier interpretation by decomposition into biologically meaningful modes of variation. The extensions to PCA are based on adaptation of maximum autocorrelation factors and the minimum noise fraction transform to shape decomposition. A common basis of the methods applied is the assessment of the annotation noise variance at individual landmarks. These assessments are based on local models or repeated annotations by independent operators.

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Author information

Authors and Affiliations

  1. Informatics and Mathematical Modelling, Technical University of Denmark, Richard Petersens Plads, Building 321, DK-2800, Kgs. Lyngby, Denmark

    Rasmus Larsen, Klaus Baggesen Hilger & Mark C. Wrobel

Authors
  1. Rasmus Larsen

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  2. Klaus Baggesen Hilger

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  3. Mark C. Wrobel

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Editor information

Editors and Affiliations

  1. Department of Mechano-informatics Graduate School of Information Science and Technology, University of Tokyo, 7-3-1 Hongo Bunkyo-ku, 113-8656, Tokyo, Japan

    Takeyoshi Dohi

  2. Department of Radiology, Brigham andWomen’s Hospital, 75 Francis St., MA, 02115, Boston, USA

    Ron Kikinis

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© 2002 Springer-Verlag Berlin Heidelberg

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Larsen, R., Hilger, K.B., Wrobel, M.C. (2002). Statistical 2D and 3D Shape Analysis Using Non-Euclidean Metrics. In: Dohi, T., Kikinis, R. (eds) Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002. MICCAI 2002. Lecture Notes in Computer Science, vol 2489. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45787-9_54

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