High-performance medical image registration using new optimization techniques
High-performance medical image registration using new optimization techniques
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DOI：
10.1109/titb.2006.864476
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发表时间：
2006-04
影响因子：
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通讯作者：
M. Wachowiak;T. Peters
中科院分区：
文献类型：
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作者：
M. Wachowiak;T. Peters
相似性度量的优化是基于强度的医学图像配准的重要组成部分。并行计算机的可用性不断提高，使得并行某些注册任务成为提高速度的有吸引力的选择。在本文中，两种新的确定性、无导数和本质并行的优化方法适用于图像配准。划分矩形 (DIRECT) 是一种用于线性有界问题的全局技术，而多向搜索 (MDS) 是一种最新的局部方法。比较了 DIRECT、MDS 和使用 Powell 方法并行实现进行局部细化的混合方法的性能。实验结果表明，DIRECT 和 MDS 稳健、准确，并且可大幅减少并行实现中的计算时间
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