3D Registration Based on Normalized Mutual Information: Performance of CPU vs. GPU Implementation
3D Registration Based on Normalized Mutual Information: Performance of CPU vs. GPU Implementation
复制标题
基于归一化互信息的 3D 配准：CPU 与 GPU 实现的性能
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
S. Wesarg
中科院分区：
文献类型：
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作者：
F. Jung;S. Wesarg
医学图像配准很耗时，但可以通过在GPU上进行并行处理来加快速度。归一化互信息（NMI）是用于执行多模态配准的性能良好的相似性度量。我们提出了基于CUDA的解决方案，计算NMI的GPU和比较通过严格注册多模态数据集与基于CPU的实现所获得的结果。我们对RIRE数据集的测试显示，我们的最佳速度为5到7倍
Medical image registration is time-consuming but can be sped up employing parallel processing on the GPU. Normalized mutual information (NMI) is a well performing similarity measure for performing multi-modal registration. We present CUDA based solutions for computing NMI on the GPU and compare the results obtained by rigidly registering multi-modal data sets with a CPU based implementation. Our tests with RIRE data sets show a speed-up of factor 5 to 7 for our best
DOI：
10.1109/dicta.2007.4426846
发表时间：
2007-12
期刊：
9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (DICTA 2007)
影响因子：
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作者：
R. Shams;Nick Barnes
通讯作者：
R. Shams;Nick Barnes