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A data distributed parallel algorithm for nonrigid image registration

机译:一种非刚性图像配准的数据分布式并行算法

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Image registration is a technique for defining a geometric relationship between each point in images. This paper presents a data distributed parallel algorithm that is capable of aligning large-scale three-dimensional (3-D) images of deformable objects. The novelty of our algorithm is to overcome the limitations on the memory space as well as the execution time. In order to enable this, our algorithm incorporates data distribution, data-parallel processing, and load balancing techniques into Schnabel's registration algorithm that realizes robust and efficient alignment based on information theory and adaptive mesh refinement. We also present some experimental results obtained on a 128-CPU cluster of PCs interconnected by Myrinet and Fast Ethernet switches. The results show that our algorithm requires less amount of memory resources, so that aligns datasets up to 1024 x 1024 x 590 voxel images with reducing the execution time from hours to minutes, a clinically compatible time.
机译:图像配准是一种用于定义图像中每个点之间的几何关系的技术。本文提出了一种数据分布式并行算法,该算法能够对齐可变形对象的大规模三维(3-D)图像。我们算法的新颖之处在于克服了对存储空间以及执行时间的限制。为了实现这一点,我们的算法将数据分发,数据并行处理和负载平衡技术纳入了Schnabel的注册算法中,该算法基于信息论和自适应网格细化实现了强大而有效的对齐。我们还介绍了通过Myrinet和快速以太网交换机互连的PC的128 CPU集群上获得的一些实验结果。结果表明,我们的算法需要较少的内存资源,因此可以对齐多达1024 x 1024 x 590个体素图像的数据集,并将执行时间从数小时缩短至数分钟(临床上可兼容的时间)。

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