Globally Optimal Multimodal Rigid Registration: An Analytic Solution using Edge Information

Current multimodal registration methods almost always rely on local gradient-descent type optimization strategies. Such registration methods often converge to an incorrect local optimum, especially when the initial misregistration is large. There are monomodal image registration methods that employ global optimization techniques. This paper introduces the use of these global optimization methods for multimodal image registration. The goal is to robustly bring the images into close enough registration that a local optimization method could fine-tune the solution. The method proposed here is based on edge information extracted from the images. Positive results from a modest set of test cases suggests that this approach is promising.