Article ID Journal Published Year Pages File Type
3073029 NeuroImage 2008 10 Pages PDF
Abstract

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 I1, I2,…,IN are generated by shooting the template J through Gaussian distributed random initial momenta θ1, θ2,…,θN. The template is J modeled as a deformation from a given hypertemplate J0 with initial momentum μ, 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.

Related Topics
Life Sciences Neuroscience Cognitive Neuroscience
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