Rapid Coarse-to-Fine Matching Using Scale-Specific Priors

Abstract

The Gibbs priors with potential equal to the membrane deflection and thin plate bending energies are explored in the Bayesian approach to image matching. Their smoothness properties are qualitatively demonstrated in a matching task. The priors are further evaluated by comparing their effect on the atlas-based localization of several subcortical structures in MRI data. Results of the localization study indicate that the implementation based on the membrane prior assumed over a fine mesh outperforms, both in speed and accuracy of the anatomic labeling, a plate-based approach that uses a comparable number of unknowns. Keywords: Image matching, Bayesian analysis, smoothness constraints, anatomic atlases, cerebral anatomy 1. INTRODUCTION Given two related images in the sense that they represent instances of the same scene, the image matching operation determines the transformation that maps each point in one image into its corresponding point in the other. Such inferences are of interest..