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GPU-Based Iterative Relative Fuzzy Connectedness Image Segmentation

Ying Zhuge, Jayaram K. Udupa, Krzysztof C. Ciesielski, Alexandre X. Falcao, Paulo A. V. Miranda, Robert W. Miller
Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892
Proc. SPIE 8316, 831604, 2012

@inproceedings{zhuge2012gpu,

   title={GPU-Based Iterative Relative Fuzzy Connectedness Image Segmentation},

   author={Zhuge, Y. and Udupa, J.K. and Ciesielski, K.C. and Falc{~a}o, A.X. and Miranda, P.A.V. and Miller, R.W.},

   booktitle={Proceedings of SPIE},

   volume={8316},

   pages={831604},

   year={2012}

}

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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, realized via 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 major computational tasks: (i) computing the fuzzy affinity relations, and (ii) computing the fuzzy connectedness relations and tracking labels for objects of interest. Both tasks are implemented as CUDA kernels, and a substantial 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-up factor of 2.4x, 17.0x, and 42.7x, correspondingly, for the three data sets on the NVIDIA Tesla C1060 over the implementation of the algorithm in CPU.
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