光谱学与光谱分析 |
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Preliminary Study on Internal Information of the Measured Tissue Based on Distributed Multi-Position Scattering Spectroscopy |
WANG Li-yun1, LI Gang2, LI Zhe2, LIN Ling2, BI Ping1, 2* |
1. Biomedical Engineering Department, Tianjin Medical University, Tianjin 300070, China 2. State Key Laboratory of Precision Measurement Technology and Instruments, Tianjin University, Tianjin 300072, China |
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Abstract The present paper describes the design of pellicle-milk double-layer phantom experiment. Milk solution of 40 different concentrations represents internal information of tissue, 1 to 5 pellicle which covers above the milk solution represents interference information of superficial tissue. The experiment collected 200 scattering spectral data of two positions and took the one single position spectral group as control, and then respectively predicted the milk solution concentration on bottom layer with the ratio of 3:1 through the BP neural network method. The experimental results show that single position scattering spectrum and two-position scattering spectrum both reached more than 90% training fitting rates and prediction accuracy, and the prediction accuracy of two-position scattering spectra is higher, reaching 98.41%. It was verified by the experimental results that scattering spectrum based on photon dissemination path can efficiently predict the milk solution concentration and eliminate the influence of superficial tissue for measurement of internal organization, and considering multi-position in modeling process can improve the accuracy of the prediction. This study validates the feasibility of the method for exploring internal information of tissue without damaging tissue integrity.
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Received: 2013-06-25
Accepted: 2013-11-05
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Corresponding Authors:
BI Ping
E-mail: biping63@126.com
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[1] LIN Ling, ZHANG Jing, XIE Xin, et al(林 凌, 张 晶, 解 鑫,等). Nanotechnology and Precision Engineering(纳米技术与精密工程), 2010,(1): 54. [2] Tzayhri Gallardo-Velazquez, Guillermo Osorio-Revilla, Marlene Zuniga-de Loa, et al. Food Research International, 2009, 42(3): 313. [3] Mignani A G, Ciaccheri L, Cucci C, et al. Sensors Journal, 2008, 8(7): 1342. [4] Tits L, Somers B, Coppin P. Geoscience and Remote Sensing, IEEE Transactions, 2012, 50(6): 2273. [5] Nhan Nguyen-Thanh, Thuc Kieu-Xuan, Insoo Koo. Wireless Communications, 2012, 11(10): 3409. [6] MIN Yi-guo, YANG Sheng-xian(闵一果, 杨声显). China Medical Engineering(中国医学工程), 2013, 21(2): 40. [7] YIN Xun-guo, LU Feng-yan, ZHANG Chao-dong(尹逊国, 卢凤艳, 张朝栋, 等). Journal of Ku nming Medical University(昆明医学院学报), 2011, 32(4): 126. [8] ZHONG Yu-mei, ZHONG Xin-gang, WANG Ye-zi, et al(钟宇眉, 钟信刚, 王叶子, 等). Chinese Journal of Infection Control(中国感染控制杂志), 2013, 12(3): 211. [9] Scafide K R N, Sheridan D J, Campbell J, et al. Forensic Science, Medicine, and Pathology, 2013. 1. [10] LIN Ling, WU Hong-jie, ZHAO Li-ying, et al(林 凌, 吴红杰, 赵丽英,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2012, 32(3): 755. [11] LI Chen-xi, ZHAO Hui-juan, WANG Zhu-lou, et al(李晨曦, 赵会娟, 王柱楼, 等). Nanotechnology and Precision Engineering(纳米技术与精密工程), 2013, 11(1): 27. [12] Trujillo S, Martinez-Torres P, Quintana P, et al. International Journal of Thermophysics, 2010, 31(4-5): 805. [13] Sawosz P, Kacprzak M, Weigl W, et al. Physics in Medicine and Biology, 2012, 57(23): 7973. [14] YE Jing-zhai, CHEN Hui(叶静斋, 陈 辉). Laser & Optoelectronics Progress(激光与光电子学进展), 2011, 48(7): 1. |
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