12769

Parallelized Seeded Region Growing using CUDA

Seongjin Park, Jeongjin Lee, Hyunna Lee, Juneseuk Shin, Jinwook Seo, Kyoung Ho Lee, Yeong-Gil Shin, Bohyoung Kim
SW Content Research Laboratory, Electronics and Telecommunications Research Institute, 218 Gajeong-Ro, Yuseong-Gu, Daejeon 305-700, Korea
Computational and Mathematical Methods in Medicine

@article{park2014parallelized,

   title={Parallelized Seeded Region Growing using CUDA},

   author={Park, Seongjin and Lee, Jeongjin and Lee, Hyunna and Shin, Juneseuk and Seo, Jinwook and Lee, Kyoung Ho and Shin, Yeong-Gil and Kim, Bohyoung},

   year={2014}

}

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This paper presents a novel method for parallelizing the seeded region growing (SRG) algorithm using Compute Unified Device Architecture (CUDA) technology, with intent to overcome the theoretical weakness of SRG algorithm of its computation time being directly proportional to the size of a segmented region. The segmentation performance of the proposed CUDA-based SRG is compared with SRG implementations on single-core, quad-core CPUs, and shader language programming, using synthetic datasets and 20 body CT scans. Based on the experimental results, the CUDA-based SRG outperforms the other three implementations, advocating that it can substantially assist the segmentation during massive CT screening tests.
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