Skip to Main Content
Optimization of a similarity metric is an essential component in most medical image registration approaches based on image intensities. In this paper, two new, deterministic, derivative-free optimization algorithms are parallelized and adapted for image registration. DIRECT (dividing rectangles) is a global technique for linearly bounded problems, and the multidirectional search (MOS) is a local method. Unlike many other deterministic optimization techniques, DIRECT and MDS allow coarse-grained parallelism. The performance of DIRECT, MDS, and hybrid methods using a fine-grained parallelization of Powell's method for local refinement, are compared. Experimental results show that DIRECT and MDS are robust, and can greatly reduce computation time in parallel implementations.
Date of Conference: 15-18 May 2005