Correspondenceless 3D-2D Registration Based on Expectation Conditional Maximization

3D-2D registration is a fundamental task in image guided interventions. Due to the physics of the X-ray imaging, however, traditional point based methods meet new challenges, where the local point features are indistinguishable, creating difficulties in establishing correspondence between 2D image feature points and 3D model points. In this paper, we propose a novel method to accomplish 3D-2D registration without known correspondences. Given a set of 3D and 2D unmatched points, this is achieved by introducing correspondence probabilities that we model as a mixture model. By casting it into the expectation conditional maximization framework, without establishing one-to-one point correspondences, we can iteratively refine the registration parameters. The method has been tested on 100 real X-ray images. The experiments showed that the proposed method accurately estimated the rotations (< 1 degrees) and in-plane (X-Y plane) translations (< 1 mm).