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

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 t...

• Type: Conference
• Year: 2007
• Where: MICCAI
• Authors: Heike Hufnagel, Xavier Pennec, Jan Ehrhardt, Heinz Handels, Nicholas Ayache
• Keywords: Generative Model Parameters, Mean Shape, Medical Imaging, MICCAI 2007, Statistical Shape Model