Abstract
A fundamental problem when computing statistical shape models (SSMs) is the determination of correspondences between the instances. Often, homologies between points that represent the surfaces are assumed which might lead to imprecise mean shape and variation results. We present a novel algorithm based on the affine Expectation Maximization - Iterative Closest Point (EM-ICP) registration method. Exact correspondences are replaced by iteratively evolving correspondence probabilities which provide the basis for the computation of mean shape and variability model. We validated our approach by computing SSMs using inexact correspondences for kidney and putamen data. In ongoing work, we want to use our methods for automatic classification applications.
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© 2007 Springer-Verlag Berlin Heidelberg
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Hufnagel, H., Pennec, X., Ehrhardt, J., Handels, H., Ayache, N. (2007). Point-Based Statistical Shape Models with Probabilistic Correspondences and Affine EM-ICP. In: Horsch, A., Deserno, T.M., Handels, H., Meinzer, HP., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2007. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71091-2_87
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DOI: https://doi.org/10.1007/978-3-540-71091-2_87
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