A Bayesian generative model for surface template estimation

Authors:
Jun Ma;Michael I. Miller;Laurent Younes
Affiliations:
Center for Imaging Science, Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD;Center for Imaging Science, Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD;Center for Imaging Science, Department of Applied Math and Statistics, The Johns Hopkins University, Baltimore, MD
Venue:
Journal of Biomedical Imaging
Year:
2010

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Abstract

3D surfaces are important geometric models for many objects of interest in image analysis and Computational Anatomy. In this paper, we describe a Bayesian inference scheme for estimating a template surface from a set of observed surface data. In order to achieve this, we use the geodesic shooting approach to construct a statistical model for the generation and the observations of random surfaces. We develop a mode approximation EM algorithm to infer the maximum a posteriori estimation of initial momentum µ, which determines the template surface. Experimental results of caudate, thalamus, and hippocampus data are presented.