Journal
NEUROIMAGE
Volume 42, Issue 1, Pages 252-261Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2008.03.056
Keywords
template estimation; computational anatomy; Bayesian; weighted Euler-Lagrange equation
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Templates play a fundamental role in Computational Anatomy. In this paper, we present a Bayesian model for template estimation. It is assumed that observed images I-1, I-2, ..., I-N are generated by shooting the template J through Gaussian distributed random initial momenta theta(1), theta(2),... theta(N). The template is J modeled as a deformation from a given hypertemplate J(0) with initial momentum mu, which has a Gaussian prior. We apply a mode approximation of the EM (MAEM) procedure, where the conditional expectation is replaced by a Dirac measure at the mode. This leads us to an image matching problem with a Jacobian weight term, and we solve it by deriving the weighted Euler-Lagrange equation. The results of template estimation for hippocampus and cardiac data are presented. (C) 2008 Elsevier Inc. All rights reserved.
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