文化大學機構典藏 CCUR:Item 987654321/30878
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    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://irlib.pccu.edu.tw/handle/987654321/30878


    题名: Coronary Arteries Segmentation Based on the 3D Discrete Wavelet Transform and 3D Neutrosophic Transform
    作者: Chen, Shuo-Tsung
    Wang, Tzung-Dau
    Lee, Wen-Jeng
    Huang, Tsai-Wei
    Hung, Pei-Kai
    Wei, Cheng-Yu
    Chen, Chung-Ming
    Kung, Woon-Man
    贡献者: Dept Exercise & Hlth Promot
    关键词: AUTOMATIC SEGMENTATION
    TEXTURE ANALYSIS
    CLASSIFICATION
    DRIVEN
    IMAGES
    RECONSTRUCTION
    MODEL
    日期: 2015
    上传时间: 2015-11-03 16:53:13 (UTC+8)
    摘要: 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.
    關聯: Biomed Research International, Volume 2015 (2015), Article ID 798303, 9 pages
    显示于类别:[運動與健康促進學系] 期刊論文

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