A Bayesian Generative Model for Surface Template Estimation

Abstract EN

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.