GPU-based relative fuzzy connectedness image segmentation.
GPU-based relative fuzzy connectedness image segmentation.
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DOI：
10.1118/1.4769418
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发表时间：
2012-12
期刊：
影响因子：
3.8
通讯作者：
Y. Zhuge;K. Ciesielski;J. Udupa;Robert W. Miller
中科院分区：
文献类型：
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作者：
Y. Zhuge;K. Ciesielski;J. Udupa;Robert W. Miller
目的是，临床放射学研究和实践越来越多，图像继续增加，数量的放射学成为现实，这是至关重要的。算法，为了段用于分段的互动速度，方法是最常见的FC段，优化了[script-l]（∞）的能量，称为相对模糊连接性（RFC），并通过rfc cos in ling can can can can can can can can can can cose contiments（rfc）。图像大小。 p-orfc（对于使用NVIDIA的计算统一设备体系结构（CUDA）平台实现的P-orfc（用于并行的最佳RFC）大大提高了上述基于CPU的IRFC算法的计算速度。 Nvidia Tesla C1060平台。这样的GPU实施在临床放射学中的自动解剖学识别中可能起着至关重要的作用。
PURPOSE Recently, clinical radiological research and practice are becoming increasingly quantitative. Further, images continue to increase in size and volume. For quantitative radiology to become practical, it is crucial that image segmentation algorithms and their implementations are rapid and yield practical run time on very large data sets. The purpose of this paper is to present a parallel version of an algorithm that belongs to the family of fuzzy connectedness (FC) algorithms, to achieve an interactive speed for segmenting large medical image data sets. METHODS The most common FC segmentations, optimizing an [script-l](∞)-based energy, are known as relative fuzzy connectedness (RFC) and iterative relative fuzzy connectedness (IRFC). Both RFC and IRFC objects (of which IRFC contains RFC) can be found via linear time algorithms, linear with respect to the image size. The new algorithm, P-ORFC (for parallel optimal RFC), which is implemented by using NVIDIA's Compute Unified Device Architecture (CUDA) platform, considerably improves the computational speed of the above mentioned CPU based IRFC algorithm. RESULTS Experiments based on four data sets of small, medium, large, and super data size, achieved speedup factors of 32.8×, 22.9×, 20.9×, and 17.5×, correspondingly, on the NVIDIA Tesla C1060 platform. Although the output of P-ORFC need not precisely match that of IRFC output, it is very close to it and, as the authors prove, always lies between the RFC and IRFC objects. CONCLUSIONS A parallel version of a top-of-the-line algorithm in the family of FC has been developed on the NVIDIA GPUs. An interactive speed of segmentation has been achieved, even for the largest medical image data set. Such GPU implementations may play a crucial role in automatic anatomy recognition in clinical radiology.