| 光谱学与光谱分析 |
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| Application of Hyperspectral Imaging Technology to Objective Diagnosis of TCM Syndrome |
| LI Jia-xing1, 2, WU Hong-jie1, LI Gang1, LIN Ling1* |
1. State Key Laboratory of Precision Measurement Technology and Instruments, Tianjin University, Tianjin 300072, China
2. College of Marine Science and Engineering, Tianjin University of Science & Technology, Tianjin 300457, China |
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Abstract Hyperspectral imaging technology is expected as a breakthrough to resolve the lack of objective indicators on the diagnosis of TCM syndrome because of its high sensitivity and including abundant information of the images and spectra. In view of fuzzy and complicated mappings between tongue and syndrome, aiming at the defects of the acquisition methods of tongue information and its processing mode which extracts the features by fragmenting the integrative information, a new idea is proposed that the specific spectral indices pool be extracted after acquiring the hyperspectral data cube of tongue by hyperspectral imaging technology and associating it as a whole because of the overlapping mixing of these characteristic information with syndrome in black-box mode by means of intergration of various linear and nonlinear data mining algorithms. The mechanisms of etiological factor and pathogenesis are analyzed from all angles by synthesis of specific spectral indices pool, clinical physiological and biochemical indicators and TCM indicators. Then a new mode of objective diagnosis for syndromes can be found.
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Received: 2010-02-16
Accepted: 2010-05-22
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Corresponding Authors:
LIN Ling
E-mail: linling@tju.edu.cn
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[1] ZHU Qing-wen, NIU Xin, YANG Xue-zhi, et al(朱庆文，牛 欣，杨学智, 等). Journal of Beijing University of Traditional Chinese Medicine(北京中医药大学学报), 2007, 30(6): 384.
[2] Jiang Lijun, Xu Wang, Chen Jianfeng. 3rd IEEE Conference on Industrial Electronics and Applications, Proceedings, 2008, S1-3: 1833.
[3] Gao Zhong, Po Laiman, Jiang Wu, et al. Third International IEEE Conference on Signal-Image Technologies and Internet-Based System, 2007, 12: 849.
[4] Vo-Dinh T, Stokes D L，Wabuyele, M B, et al. IEEE Engineering in Medicine and Biology Magazine, 2004, 23(5): 40.
[5] Erives H, Targhetta N B. IEEE Transactions on Instrumentation and Measurement, 2009, 58(3): 631.
[6] Martin M E, Wabuyele M B, Chen K, et al. Annals of Biomedical Engineering, 2006, 34(6): 1061.
[7] Stamatas G N, Southall M, Kollias N. Journal of Investigative Dernatology, 2006, 126(8): 1753.
[8] LI Qing-li, XUE Yong-qi, WANG Jian-yu, et al(李庆利，薛永祺，王建宇, 等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2006, 25(6): 465.
[9] LI Qing-li, XUE Yong-qi, LIU Zhi, et al(李庆利，薛永祺，刘 治, 等). Opto-Electronic Engineering(光电工程), 2007, 34(4): 60.
[10] Li Qingli, Liu Zhi. Computerized Medical Imaging and Graphics, 2009, 33(3): 217.
[11] LI Qing-li, XUE Yong-qi, WANG Jian-yu, et al(李庆利，薛永祺，王建宇, 等). Journal of Biomedical Engineering(生物医学工程学杂志), 2008, 25(2): 368.
[12] Goetz A F H, Rowan L C. Science, 1981, 221: 781.
[13] Ellis R J, Scott P W. Remote Sensing of Environment, 2004, 93(1-2): 118.
[14] Lucas Kelly L, Carter Gregory A. Remote Sensing of Environment, 2008, 112(10): 3908.
[15] Goetz Alexander F H. Remote Sensing of Environment, 2009, 113: S5.
[16] Nguyen H T, Lee B W. European Journal of Agronomy, 2006, 24(4): 349.
[17] LI Shao(李 梢). Journal of Beijing University of Traditional Chinese Medicine(北京中医药大学学报), 2003, 26 (3): 1.
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