Shape Analysis Using a Point-Based Statistical Shape Model Built on Correspondence Probabilities

Heike Hufnagel, Xavier Pennec, Jan Ehrhardt, Heinz Handels, Nicholas Ayache


A fundamental problem when computing statistical shape models is the determination of correspondences between the instances of the associated data set. Often, homologies between points that represent the surfaces are assumed which might lead to imprecise mean shape and variability results. We propose an approach where exact correspondences are replaced by evolving correspondence probabilities. These are the basis for a novel algorithm that computes a generative statistical shape model. We developed an unified MAP framework to compute the model parameters ('mean shape' and 'modes of variation') and the nuisance parameters which leads to an optimal adaption of the model to the set of observations. The registration of the model on the instances is solved using the Expectation Maximization - Iterative Closest Point algorithm which is based on probabilistic correspondences and proved to be robust and fast. The alternated optimization of the MAP explanation with respect to the observation and the generative model parameters leads to very efficient and closed-form solutions for (almost) all parameters. Experimental results on brain structure data sets demonstrate the efficiency and well-posedness of the approach. The algorithm is then extended to an automatic classification method using the k-means clustering and applied to synthetic data as well as brain structure classification problems.

Original languageEnglish
Title of host publicationMICCAI 2007: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007
Number of pages9
Place of PublicationBerlin, Heidelberg
PublisherSpringer Verlag
Publication date01.12.2007
ISBN (Print)978-3-540-75756-6
ISBN (Electronic)978-3-540-75757-3
Publication statusPublished - 01.12.2007
Event10th International Conference on Medical Imaging and Computer-Assisted Intervention - Brisbane, Australia
Duration: 29.10.200702.11.2007
Conference number: 71013


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