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GPU-based parallel fuzzy c-mean clustering model via genetic algorithm

机译：基于遗传算法的基于GPU的并行模糊c均值聚类模型

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摘要

Detection of white matter changes in brain tissue using magnetic resonance imaging has been an increasingly active and challenging research area in computational neuroscience. A genetic algorithm based on a fuzzy c-mean clustering method (GAFCM) was applied to simulated images to separate foreground spot signal information from the background, and the results were compared. The strength of this algorithm was tested by evaluating the segmentation matching factor, coefficient of determination, concordance correlation, and gene expression values. The experimental results demonstrated that the segmentation ability of GAFCM was better than that of fuzzy c-means and K-means algorithms. However, GAFCM is computationally expensive. This study presents a new GPU-based parallel GAFCM algorithm to improve the performance of GAFCM. The experimental results show that computational performance can be increased by a factor of approximately 20 over the CPU-based GAFCM algorithm while maintaining the quality of the processed images. Thus, the proposed GPU-based parallel GAFCM algorithm can achieve the same results and significantly decrease processing time. Copyright © 2015 John Wiley & Sons, Ltd.
机译：使用磁共振成像检测脑组织中的白质变化已成为计算神经科学领域中越来越活跃和具有挑战性的研究领域。将基于模糊c均值聚类方法（GAFCM）的遗传算法应用于模拟图像，以将前景点信号信息与背景分离，并对结果进行比较。通过评估分段匹配因子，确定系数，一致性相关性和基因表达值来测试该算法的强度。实验结果表明，GAFCM的分割能力优于模糊c均值和K均值算法。但是，GAFCM在计算上很昂贵。这项研究提出了一种新的基于GPU的并行GAFCM算法，以提高GAFCM的性能。实验结果表明，与基于CPU的GAFCM算法相比，计算性能可以提高大约20倍，同时保持处理后图像的质量。因此，提出的基于GPU的并行GAFCM算法可以实现相同的结果，并显着减少处理时间。版权所有©2015 John Wiley＆Sons，Ltd.

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