本團隊在根據漫反射光譜學(Diffuse Reflectance Spectroscopy，簡稱DRS) 建立之光學量測系統已達成非侵入、快速定量人體組織的光學特性的效果。但接觸式的量測在臨床上有著壓迫患部及交叉感染等問題，為了有效改善此點以及提昇臨床應用度，在本研究中以先前接觸式DRS 系統為根基，發展了一套簡易的非接觸式DRS量測系統。以往接觸式系統透過蒙地卡羅法與人工類神經網路結合的演算法，大幅縮短數值處理的時間，並以假體校正的方式即時輸出人體組織的光學特性。本研究開發的非接觸系統延續了接觸式系統的架構與優點，並增設影像輔助裝置提供使用者判斷量測距離是否理想，亦可觀察待測物表面的現象。最後將量測系統整體的控制與計算整合，且使用另一種程式語言—Python 進行開發，取代原有的Matlab 編程，並移植到單板電腦—樹梅派2 代，以大幅縮小控制系統的體積。此系統通過模擬與假體驗證後，即進行人體皮膚的實測，並分析黑色素與組織血氧濃度的結果。其數據表現與現實情況相符合，黑色素濃度與不同的膚色表徵趨勢相同，而組織血氧濃度隨著靜脈閉鎖後有所下降，雖然偵測範圍相較於接觸式更為淺層，但其準確的定量以及便利的輔助功能相信能在未來作為臨床醫療人員一項優良的解決方案。

In this study, we develop a simple non-contact optical measurement system that based on the contact diffuse reflectance spectrometry system for clinical measurement. For calculating optical characteristics of human tissues, our contact system greatly reduced the numerical processing time by Monte Carlo method and artificial neural network algorithm. Simple non-contact system inherits the structure and advantages of contact system. Further, it increases the camera to provide users to determine whether the ideal distance measurement and observe the surface of the test object. Finally, this system is written in Python, and used Raspberry Pi 2 and 4-inch TFT-LCD screen as a hub in order to reduce the size of the control system. After the system through the simulation and prosthesis verification, we measure human skin and analyze melanin and tissue oxygen concentration. The result is in line with the actual situation. Melanin concentration with different skin tone the same trend, and tissue oxygen concentration decreased with vein occlusion. Although the detection range is more superficial than contact system, the accurate quantification and convenient accessibility of this system are believed to be an excellent solution for clinical in the future.

[1] John YK Lee, Jayesh P Thawani, John Pierce, Ryan Zeh, Maria Martinez-Lage, Michelle Chanin, Ollin Venegas, Sarah Nims, Kim Learned, and Jane Keating. Intraoperative near-infrared optical imaging can localize gadolinium-enhancing gliomas during surgery. Neurosurgery, 79(6):856–871, 2016.
[2] David Huang, Eric A Swanson, Charles P Lin, Joel S Schuman, William G Stinson, Warren Chang, Michael R Hee, Thomas Flotte, Kenton Gregory, and Carmen A Puliafito. Optical coherence tomography. Science (New York, NY), 254(5035):1178, 1991.
[3] Sheng-Hao Tseng, Alexander Grant, and Anthony J Durkin. In vivo determination of skin near-infrared optical properties using diffuse optical spectroscopy. Journal of biomedical optics, 13(1):014016–014016, 2008.
[4] Thomas J Farrell, Michael S Patterson, and Brian Wilson. A diffusion theory model of spatially resolved, steady-state diffuse reflectance for the noninvasive determination of tissue optical properties in vivo. Medical physics, 19(4):879–888, 1992.
[5] Tuan H Pham, Olivier Coquoz, Joshua B Fishkin, Eric Anderson, and Bruce J Tromberg. Broad bandwidth frequency domain instrument for quantitative tissue optical spectroscopy. Review of Scientific Instruments, 71(6):2500–2513, 2000.
[6] SJ Matcher, M Cope, and DT Delpy. In vivo measurements of the wavelength dependence of tissue-scattering coefficients between 760 and 900 nm measured with timeresolved spectroscopy. Applied Optics, 36(1):386–396, 1997.
[7] RMP Doornbos, Roland Lang, MC Aalders, FW Cross, and HJCM Sterenborg. The determination of in vivo human tissue optical properties and absolute chromophore concentrations using spatially resolved steady-state diffuse reflectance spectroscopy. Physics in medicine and biology, 44(4):967, 1999.
[8] George Zonios, Lev T Perelman, Vadim Backman, Ramasamy Manoharan, Maryann Fitzmaurice, Jacques Van Dam, and Michael S Feld. Diffuse reflectance spectroscopy of human adenomatous colon polyps in vivo. Applied Optics, 38(31):6628–6637, 1999.
[9] JA Delgado Atencio, EE Orozco Guillén, S Vázquez y Montiel, M Cunill Rodríguez, J Castro Ramos, JL Gutiérrez, and F Martínez. Influence of probe pressure on human skin diffuse reflectance spectroscopy measurements. Optical Memory and Neural Networks, 18(1):6–14, 2009.
[10] Eric K Chan, Brian Sorg, Dmitry Protsenko, Michael O’Neil, Massoud Motamedi, and Ashley J Welch. Effects of compression on soft tissue optical properties. IEEE Journal of selected topics in quantum electronics, 2(4):943–950, 1996.
[11] Wenliang Chen, Rong Liu, Kexin Xu, and Ruikang K Wang. Influence of contact state on nir diffuse reflectance spectroscopy in vivo. Journal of Physics D: Applied Physics, 38(15):2691, 2005.
[12] Blaž Cugmas, Miran Bürmen, Maksimilijan Bregar, Franjo Pernuš, and Boštjan Likar. Pressure-induced near infrared spectra response as a valuable source of information for soft tissue classification. Journal of biomedical optics, 18(4):047002–047002, 2013.
[13] Roberto Reif, Mark S Amorosino, Katherine W Calabro, Ousama A’Amar, Satish K Singh, and Irving J Bigio. Analysis of changes in reflectance measurements on biological tissues subjected to different probe pressures. Journal of biomedical optics, 13(1):010502–010502, 2008.
[14] Veronica Sorgato, Michel Berger, Charlotte Emain, Christine Vever-Bizet, Jean-Marc Dinten, Geneviève Bourg-Heckly, and Anne Planat-Chrétien. Aca-pro: calibration protocol for quantitative diffuse reflectance spectroscopy. validation on contact and noncontact probe-and ccd-based systems. Journal of biomedical optics, 21(6):065003– 065003, 2016.
[15] 郭俊言. 研究具有調整雙層式或傳統式量測架構配置的平台式與手持式漫反射光譜系統之性能表現, 07 2015.
[16] Lihong Wang and Steven L Jacques. Monte carlo modeling of light transport in multilayered tissues in standard c. The University of Texas, MD Anderson Cancer Center, Houston, pages 4–11, 1992.
[17] Lihong V Wang and Hsin-i Wu. Biomedical optics: principles and imaging. John Wiley & Sons, 2012.
[18] Tiziano Binzoni, TS Leung, AH Gandjbakhche, Daniel Ruefenacht, and DT Delpy. The use of the henyey–greenstein phase function in monte carlo simulations in biomedical optics. Physics in medicine and biology, 51(17):N313, 2006.
[19] Yu-Wen Chen and Sheng-Hao Tseng. Efficient construction of robust artificial neural networks for accurate determination of superficial sample optical properties. Biomedical optics express, 6(3):747–760, 2015.
[20] Corinna Cortes and Vladimir Vapnik. Support-vector networks. Machine learning, 20(3):273–297, 1995.
[21] S Rasoul Safavian and David Landgrebe. A survey of decision tree classifier methodology. IEEE transactions on systems, man, and cybernetics, 21(3):660–674, 1991.
[22] Matt W Gardner and SR Dorling. Artificial neural networks (the multilayer perceptron)— a review of applications in the atmospheric sciences. Atmospheric environment, 32(14):2627–2636, 1998.
[23] Andrew Ng. Sparse autoencoder. CS294A Lecture notes, 72(2011):1–19, 2011.
[24] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105, 2012.
[25] Tomas Mikolov, Martin Karafiát, Lukas Burget, Jan Cernockỳ, and Sanjeev Khudanpur. Recurrent neural network based language model. In Interspeech, volume 2, page 3, 2010.
[26] Wikimedia Commons. File:complete neuron cell diagram zh-hant.svg — wikimedia commons,, 2017. [Online; accessed 20-十一月-2017].
[27] Frank Rosenblatt. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological review, 65(6):386, 1958.
[28] Robert Hecht-Nielsen. Theory of the backpropagation neural network. Neural Networks, 1(Supplement-1):445–448, 1988.
[29] Léon Bottou. Large-scale machine learning with stochastic gradient descent. In Proceedings of COMPSTAT’2010, pages 177–186. Springer, 2010.
[30] Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
[31] John Duchi, Elad Hazan, and Yoram Singer. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, 12(Jul):2121–2159, 2011.
[32] Ning Qian. On the momentum term in gradient descent learning algorithms. Neural networks, 12(1):145–151, 1999.
[33] Richard HR Hahnloser, Rahul Sarpeshkar, Misha A Mahowald, Rodney J Douglas, and H Sebastian Seung. Digital selection and analogue amplification coexist in a cortexinspired silicon circuit. Nature, 405(6789):947, 2000.
[34] Torre M Bydlon, Rami Nachabé, Nimmi Ramanujam, Henricus JCM Sterenborg, and Benno HW Hendriks. Chromophore based analyses of steady-state diffuse reflectance spectroscopy: current status and perspectives for clinical adoption. Journal of biophotonics, 8(1-2):9–24, 2015.
[35] Sheng-Hao Tseng, Chao-Kai Hsu, Julia Yu-Yun Lee, Shih-Yu Tzeng, Wan-Rung Chen, and Yu-Kai Liaw. Noninvasive evaluation of collagen and hemoglobin contents and scattering property of in vivo keloid scars and normal skin using diffuse reflectance spectroscopy: pilot study. Journal of biomedical optics, 17(7):0770051–07700511, 2012.
[36] Georgios N Stamatas and Nikiforos Kollias. Blood stasis contributions to the perception of skin pigmentation. Journal of biomedical optics, 9(2):315–322, 2004.