Parallelized Seeded Region Growing Using CUDA-Reference-Cited by-同舟云学术

Parallelized Seeded Region Growing Using CUDA

Published:2014 Issue: Volume:2014 Page:1-10
ISSN:1748-670X
Container-title:Computational and Mathematical Methods in Medicine
language:en
Short-container-title:Computational and Mathematical Methods in Medicine

Author:

Park Seongjin¹,Lee Jeongjin²^ORCID,Lee Hyunna³,Shin Juneseuk⁴^ORCID,Seo Jinwook⁵,Lee Kyoung Ho⁶,Shin Yeong-Gil⁵,Kim Bohyoung⁷

Affiliation:

1. SW Content Research Laboratory, Electronics and Telecommunications Research Institute, 218 Gajeong-Ro, Yuseong-Gu, Daejeon 305-700, Republic of Korea

2. School of Computer Science & Engineering, Soongsil University, 369 Sangdo-Ro, Dongjak-Gu, Seoul 156-743, Republic of Korea

3. Department of Brain and Cognitive Sciences, Seoul National University, 1 Gwanak-Ro, Gwanak-Gu, Seoul 151-742, Republic of Korea

4. Department of Systems Management Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do 440-746, Republic of Korea

5. School of Computer Science and Engineering, Seoul National University, 599 Kwanak-ro, Kwanak-gu, Seoul 151-742, Republic of Korea

6. Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, 82 Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do 463-707, Republic of Korea

7. Department of Radiology, Seoul National University Bundang Hospital, 82 Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do 463-707, Republic of Korea

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.

Funder

National Research Foundation of Korea

Publisher

Hindawi Limited

Subject

Applied Mathematics,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,Modeling and Simulation,General Medicine

Link

http://downloads.hindawi.com/journals/cmmm/2014/856453.pdf

Reference19 articles.

1. Snakes: Active contour models

2. Seeded region growing

3. An improved seeded region growing algorithm

4. A semi-automatic seeded region growing algorithm for video object localization and tracking

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