publication . Article . 2015

A GPU-based implementations of the fuzzy C-means algorithms for medical image segmentation

Mahmoud Al-Ayyoub; Ansam M. Abu-Dalo; Yaser Jararweh; Moath Jarrah; Mohammad Al Sa’d;
  • Published: 23 Apr 2015 Journal: The Journal of Supercomputing, volume 71, pages 3,149-3,162 (issn: 0920-8542, eissn: 1573-0484, Copyright policy)
  • Publisher: Springer Science and Business Media LLC
Fuzzy clustering is one of the most popular techniques in medical image segmentation. The fuzzy C-means (FCM) algorithm has been widely used as it provides better performance and more information than other algorithms. As the data set becomes large, the serial implementation of the FCM algorithm becomes too slow to accomplish the clustering task within acceptable time. Hence, a parallel implementation [for example, using today's fast graphics processing unit (GPU)] is needed. In this paper, we implement brFCM algorithm, a faster variant of the FCM algorithm, on two different GPU cards, Tesla M2070 and Tesla K20m. We compare our brFCM GPU-based implementation wit...
ACM Computing Classification System: ComputingMethodologies_PATTERNRECOGNITIONComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Theoretical Computer Science, Hardware and Architecture, Software, Information Systems, Graphics processing unit, Medical imaging, Implementation, Fuzzy logic, Algorithm, Fuzzy clustering, Image segmentation, Computer science, Cluster analysis, Parallel computing
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