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Proceedings Paper

GPU-based iterative relative fuzzy connectedness image segmentation
Author(s): Ying Zhuge; Jayaram K. Udupa; Krzysztof C. Ciesielski; Alexandre X. Falcão; Paulo A. V. Miranda; Robert W. Miller
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Paper 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, 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.

Paper Details

Date Published: 16 February 2012
PDF: 11 pages
Proc. SPIE 8316, Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling, 831604 (16 February 2012); doi: 10.1117/12.911794
Show Author Affiliations
Ying Zhuge, National Institutes of Health (United States)
Jayaram K. Udupa, Univ. of Pennsylvania (United States)
Krzysztof C. Ciesielski, Univ. of Pennsylvania (United States)
West Virginia Univ. (United States)
Alexandre X. Falcão, Univ. of Campinas (Brazil)
Paulo A. V. Miranda, Univ. of São Paulo (Brazil)
Robert W. Miller, National Institutes of Health (United States)


Published in SPIE Proceedings Vol. 8316:
Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling
David R. Holmes; Kenneth H. Wong, Editor(s)

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