High Performance Derivative-Free Optimization Applied to Biomedical Image Registration

M.P. Wachowiak, T.M. Peters
19th International Symposium on High Performance Computing Systems and Applications (HPCS'05)  
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 (MDS) is a local method. Unlike many other deterministic optimization techniques, DIRECT and MDS allow coarse-grained
more » ... 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.
doi:10.1109/hpcs.2005.31 dblp:conf/hpcs/WachowiakP05 fatcat:l2quohhywnf2lfxqzpd54kec24