High-performance medical image registration using new optimization techniques.

Mark P Wachowiak; Terry M Peters

doi:10.1109/titb.2006.864476

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High-performance medical image registration using new optimization techniques.

Mark P Wachowiak·Terry M Peters

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Abstract

Optimization of a similarity metric is an essential component in intensity-based medical image registration. The increasing availability of parallel computers makes parallelizing some registration tasks an attractive option to increase speed. In this paper, two new deterministic, derivative-free, and intrinsically parallel optimization methods are adapted for image registration. DIviding RECTangles (DIRECT) is a global technique for linearly bounded problems, and multidirectional search (MDS) is a recent local method. The performance of DIRECT, MDS, and hybrid methods using a parallel implementation of Powell's method for local refinement, are compared. Experimental results demonstrate that DIRECT and MDS are robust, accurate, and substantially reduce computation time in parallel implementations.

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Algorithms Artificial Intelligence Humans Image Enhancement Image Interpretation, Computer-Assisted Magnetic Resonance Imaging Pattern Recognition, Automated Subtraction Technique Evaluation Study Journal Article Research Support, Non-U.S. Gov't

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Mark P Wachowiak, Terry M Peters. 2006. High-performance medical image registration using new optimization techniques.. https://doi.org/10.1109/titb.2006.864476

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