Highly-Automatic MI Based Multiple 2D/3D Image Registration Using Self-initialized Geodesic Feature Correspondences

Intensity based registration methods, such as the mutual information (MI), do not commonly consider the spatial geometric information and the initial correspondences are uncertainty. In this paper, we present a novel approach for achieving highly-automatic 2D/3D image registration integrating the advantages from both entropy MI and spatial geometric features correspondence methods. Inspired by the scale space theory, we project the surfaces on a 3D model to 2D normal image spaces provided that it can extract both local geodesic feature descriptors and global spatial information for estimating initial correspondences for image-to-image and image-to-model registration. The multiple 2D/3D image registration can then be further refined using MI. The maximization of MI is effectively achieved using global stochastic optimization. To verify the feasibility, we have registered various artistic 3D models with different structures and textures. The high-quality results show that the proposed approach is highly-automatic and reliable.