title: A bayesian generative model for surface template estimation.
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.
authors.affiliation: Center for Imaging Science, Department of Biomedical Engineering, The Johns Hopkins University, 320 Clark Hall, Baltimore, MD 21218, USA.
authors.forename: Jun
authors.initials: J
authors.lastname: Ma
authors.forename: Michael I
authors.initials: MI
authors.lastname: Miller
authors.forename: Laurent
authors.initials: L
authors.lastname: Younes