医学影像模糊连通分割的优化与并行化

Christopher Gammage, V. Chaudhary
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引用次数: 3

摘要

模糊连通性是医学图像处理中一种重要的图像分割方法。它通常用于手术前准备,有时也用于手术中。重要的是要有一个算法,可以执行非常快,特别是在术中环境。我们从名为ITK的流行图像处理工具包中获取代码,并将其移植到C环境中。我们对实现进行了优化,以获得最大的性能(提供23倍的加速)。我们尝试了三种不同级别的并行化。我们发现MPI不是一种有效的并行化方法,因为该算法依赖于数据,并且必须进行大量的通信。这种通信掩盖了在多个处理器或集群中的节点上进行计算所带来的速度提升。然而,在SMP系统上使用OpenMP获得了一些有限的优化加速,导致使用4个处理器的速度比原始ITK实现提高了50%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On optimization and parallelization of fuzzy connected segmentation for medical imaging
Fuzzy Connectedness is an important image segmentation routine for image processing of medical images. It is often used in preparation for surgery and sometimes during surgery. It is important to have an algorithm which can execute very fast, especially in the intra-operative environment. We have taken code from a popular image processing toolkit called ITK and ported it to a C environment. We optimized the implementation to give maximal performance (giving speedup of 23 times). We attempted three different levels of parallelization. We found that MPI was not an efficient method of parallelization as the algorithm is data dependant and large amounts of communication must be done. This communication overshadows the speed increase from doing computation on multiple processors, or nodes in a cluster. However, some limited speedup over the optimizations was obtained using OpenMP on an SMP system leading to a speedup of fifty using four processors over the original ITK implementation.
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