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BioMed Research International
Volume 2015 (2015), Article ID 798303, 9 pages
Research Article

Coronary Arteries Segmentation Based on the 3D Discrete Wavelet Transform and 3D Neutrosophic Transform

1Institute of Biomedical Engineering, National Taiwan University, Taipei 10617, Taiwan
2Department of Applied Mathematics, Tunghai University, Taichung 40704, Taiwan
3Sustainability Research Center, Tunghai University, Taichung 40704, Taiwan
4Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital, Taipei 10048, Taiwan
5Department of Medical Imaging, National Taiwan University Hospital, Taipei 10048, Taiwan
6Department of Nursing, College of Medicine and Nursing, Hungkuang University, Taichung 43302, Taiwan
7Department of Exercise and Health Promotion, College of Education, Chinese Culture University, Taipei 11114, Taiwan
8Department of Neurology, Chang Bing Show Chwan Memorial Hospital, Changhua 50544, Taiwan
9Department of Neurosurgery, Lo-Hsu Foundation, Lotung Poh-Ai Hospital, Luodong, Yilan 26546, Taiwan

Received 19 July 2014; Accepted 11 October 2014

Academic Editor: Kuo-Sheng Hung

Copyright © 2015 Shuo-Tsung Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Purpose. Most applications in the field of medical image processing require precise estimation. To improve the accuracy of segmentation, this study aimed to propose a novel segmentation method for coronary arteries to allow for the automatic and accurate detection of coronary pathologies. Methods. The proposed segmentation method included 2 parts. First, 3D region growing was applied to give the initial segmentation of coronary arteries. Next, the location of vessel information, HHH subband coefficients of the 3D DWT, was detected by the proposed vessel-texture discrimination algorithm. Based on the initial segmentation, 3D DWT integrated with the 3D neutrosophic transformation could accurately detect the coronary arteries. Results. Each subbranch of the segmented coronary arteries was segmented correctly by the proposed method. The obtained results are compared with those ground truth values obtained from the commercial software from GE Healthcare and the level-set method proposed by Yang et al., 2007. Results indicate that the proposed method is better in terms of efficiency analyzed. Conclusion. Based on the initial segmentation of coronary arteries obtained from 3D region growing, one-level 3D DWT and 3D neutrosophic transformation can be applied to detect coronary pathologies accurately.