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Journal Article
Parallelized Seeded Region Growing Using CUDA
- Authors
- Seongjin Park, Jeongjin Lee, Hyunna Lee, Juneseuk Shin, Jinwook Seo, Kyoung Ho Lee, Yeong-Gil Shin, Bohyoung Kim
- Issue Date
- 2014-09
- Citation
- Computational and Mathematical Methods in Medicine, v.2014, pp.1-10
- ISSN
- 1748-670X
- Publisher
- Hindawi Publishing
- Language
- English
- Type
- Journal Article
- Abstract
- This paper presents a novel method for parallelizing the seeded region growing (SRG) algorithm using Compute Unified Device Architecture (CUDA) technology, with intention 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 CPUs, 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.
- KSP Keywords
- CT scan, Compute Unified Device Architecture(CUDA), Ct screening, Screening tests, Seeded region growing, Synthetic Datasets, computation time, novel method, quad-core, single core
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
