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- W2146730780 abstract "This paper presents a parallel algorithm for the top of the line among the fuzzy connectedness algorithm family,namely the iterative relative fuzzy connectedness (IRFC) segmentation method. The algorithm of IRFC, realizedvia image foresting transform (IFT), is implemented by using NVIDIA's compute unified device architecture(CUDA) platform for segmenting large medical image data sets. In the IRFC algorithm, there are two majorcomputational tasks: (i) computing the fuzzy affinity relations, and (ii) computing the fuzzy connectednessrelations and tracking labels for objects of interest. Both tasks are implemented as CUDA kernels, and asubstantial improvement in speed for both tasks is achieved. Our experiments based on three data sets of small,medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-upfactor of 2.4x, 17.0x, and 42.7x, correspondingly, for the three data sets on the NVIDIA Tesla C1060 over theimplementation of the algorithm in CPU." @default.
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- W2146730780 date "2012-02-23" @default.
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- W2146730780 title "GPU-based iterative relative fuzzy connectedness image segmentation" @default.
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- W2146730780 doi "https://doi.org/10.1117/12.911794" @default.
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