臺灣博碩士論文加值系統

米繞式全域式光學同調斷層掃描術(Full-field optical coherence tomography; FF-OCT)具有高解析度、低震動雜訊與良好色散補償優勢，可用來觀察樣本形貌與強度分布資訊，但無法得知該樣本的化學組成；近紅外光拉曼光譜儀則具備量測樣本內化學分子組成且低螢光影響與穿透深度較深等優點，但無法獲得樣本的微結構影像。因此將兩者搭配應用，量測體外(in vitro)的五種皮膚細胞，包括角質細胞株、基底細胞癌細胞株、鱗狀細胞癌細胞株、黑色素細胞與黑色素癌細胞株，可同時獲得細胞的微結構、亮暗分布以及細胞組成化學分子資訊。而在臨床應用上，為提高判斷精確度與速度，利用機器學習的整體學習法進行演算與分類。
在in vitro實驗中，就FF-OCT三維影像上，發現癌細胞群的表面皆有明顯的突起，可能與其侵略性相關；在亮度上，角質系列的正常細胞表面較平滑且內部均質而較低。利用軟體半自動擷取出共283顆不同種細胞的三維資訊，含體積、致密度、表面粗糙度、內部平均亮度及內部亮度分布標準差，結果顯示後三項特徵，可以成為分辨癌細胞與否的良好指標，而平均亮度可用來區分出正常細胞中的黑色素細胞與角質細胞。對於癌細胞的分類仍可用致密度與體積的平均值做區分，但其標準差較大，無法成為良好指標，因此須搭配拉曼光譜儀。
在in vitro五種細胞株的拉曼頻譜中，顯現正常細胞的拉曼頻譜相較於癌細胞標準差較大，其可能是生物特性所致，正常細胞不像癌細胞單種繁衍，可能存在多種變異。而癌細胞的頻譜標準差相對較小，且能與文獻對照幾乎相符。在600-2000 cm-1拉曼頻譜中，可用6根peaks與4組bands (Peaks: 746, 780, 857, 1024, 1063, 1209 cm-1; Bands: 925-946, 990-1010, 1088-1130, 1281-1302 cm-1) / 7根peaks與3組bands (Peaks: 854, 898, 1064, 1158, 1191, 1233, 1452 cm-1; Bands: 923-946, 1007-1028, 1291-1336 cm-1)分別區分黑色素細胞癌對角質系列細胞癌 / 基底細胞癌對鱗狀細胞癌，因此在癌細胞分類上拉曼頻譜有較佳的表現。
藉由機器學習中的整體學習法進行分類，也能驗證以上所說。使用整體學習法中的決策樹分析法普遍有較好的分類效果，FF-OCT特徵在區分正常細胞與癌細胞可得準確度為85.9%，且在分辨正常細胞種類上可到達完全區分；而拉曼頻譜則是在分類三種癌細胞群上可達到完全區分。因此搭配FF-OCT與紅外光拉曼技術，並結合機器學習，可以很準確且快速的辨別五種正常與癌化皮膚細胞株，顯示此FF-OCT與紅外光拉曼光學技術搭配整體學習演算法可在皮膚科臨床研究與應用上成為重要的診斷工具與方向。

Mirau-based full-field optical coherence tomography (FF-OCT) has many advantages such as high resolution, low vibration noise, and good dispersion compensation. Thus it can observe the morphology and intensity distribution of the sample but not its chemical component. Near-infrared (NIR) Raman spectroscopy can recognize the chemical institution of the sample with the advantages of low fluorescent effect and deep penetration, but not its microstructure. Therefore, measuring the five skin in vitro cell lines, including Keratinocyte cell lines, Basal cell carcinoma cell lines, Squamous cell carcinoma cell lines, Melanocyte cell and Melanoma cell lines, through combining both two techniques can acquire the microstructure, intensity distribution and chemical molecular information inside cells. Then in order to increase the accuracy and speed of the judgment in the clinical application, we use the ensemble learning algorithm of machine learning to calculate and classify them.
In the in vitro experiments, the FF-OCT three-dimensional (3D) images of the five skin cell lines show the obvious protrusion on the surface of cancerous cells, that may relate to the aggressiveness of cell. And due to the smoother surface and more homogeneous internal space of the normal keratinocyte cell lines, they have lower average intensity. We utilized software to semi-automatically capture totally 283 different cells’ 3D information, including volume, compactness, surface roughness, internal average intensity, and internal intensity standard deviation. The results imply the last three features can be great parameters to distinguish between cancerous and normal cells, and the internal average intensity can be used to classify the normal melanocyte cell and keratinocyte cell. The features included compactness and volume can also distinguish different cancerous cells, but the standard deviation of these two features is too large, thus they can not be excellent indicators for classification. As a result, it needs combining the Raman spectroscopy.
On the Raman spectrum of different in vitro skin cell lines, they indicate the standard deviation of the Raman spectrum from normal cells is larger than ones from cancerous cells, that may due to the biological characteristics of normal cells, that is to say, normal cells have larger variation because the cancerous cells reproduce them within one species. On the other hand, the standard deviation of Raman spectrum form cancerous cells is smaller, and also these Raman spectrums are seemingly corresponding with the literature. We can successfully classify the melanoma cells with keratinocyte-based cancerous cells / the basal cell carcinoma cells with the squamous cell carcinoma cells by six peaks and four bands (Peaks: 746, 780, 857, 1024, 1063, 1209 cm-1; Bands: 925-946, 990-1010, 1088-1130, 1281-1302 cm-1) / seven peaks and three bands (Peaks: 854, 898, 1064, 1158, 1191, 1233, 1452 cm-1; Bands: 923-946, 1007-1028, 1291-1336 cm-1) in the Raman spectrum of 600-2000 cm-1. Consequently, the classification of Raman spectrums on cancerous cells has better performance.
Eventually, we employ the ensemble learning form machine learning to classify them, which also verify the above results. Decision tree method in ensemble learning has better-classified results generally. The accuracy can reach 85.9% on distinguishing normal and cancerous cell by FF-OCT features. Also, these features can distinguish every species of normal skin cell. Then Raman spectrums can completely classify three kinds of cancerous skin cell. Therefore, these techniques can distinguish these five normal and cancerous skin cell-lines very accurately and fast. And it shows the method of integrating FF-OCT, NIR Raman spectroscopy and ensemble learning can be an important diagnostic tool and direction for clinical research and application.

致謝 I
摘要 II
Abstract IV
目錄 VI
圖目錄 VIII
表目錄 XIII
第一章 緒論 1
1.1背景與研究動機 1
1.2本文內容概述 3
第二章 Mirau-based全域式光學同調斷層掃描 4
2.1光學同調斷層掃描基本原理 4
2.2摻鈰釔鋁石榴石晶體光纖寬頻光源 11
2.3 Mirau-based全域式光學同調斷層掃描系統 16
2.3.1 Mirau-based FF-OCT特性與系統架構 16
2.3.2 Mirau組件製作方法 21
2.3.3 FF-OCT系統之縱向、橫向解析度 23
2.3.4 OCT影像處理方法 28
第三章 拉曼散射原理、系統及皮膚組成資訊 30
3.1拉曼散射基本原理 30
3.2近紅外光拉曼系統簡介與頻譜處理 36
3.2.1近紅外光拉曼系統架構與元件特性 37
3.2.2拉曼頻譜處理與訊噪比 39
3.3皮膚與其保濕主要成分之拉曼特性 46
3.3.1皮膚功能與其分層結構 46
3.3.2皮膚保濕成分介紹與其拉曼特性量測 51
第四章 皮膚細胞株判別之OCT影像結果與分析 61
4.1皮膚癌與皮膚細胞株介紹 61
4.2細胞樣本製備 66
4.2.1樣本製備 66
4.2.2 OCT影像量測方法 68
4.3實驗結果 69
4.4細胞株之三維形貌特徵分析 73
4.4.1影像處理與分析方法 73
4.4.2影像特徵選取與分析結果 83
第五章 機器學習應用於細胞判別之OCT三維影像特徵結合拉曼頻譜分析 93
5.1整體學習法 93
5.2皮膚細胞之拉曼頻譜辨別分析 106
5.2.1樣本製備 106
5.2.2頻譜量測方法 107
5.2.3結果與分析 108
5.3整體學習法應用於區分皮膚細胞 114
5.3.1 OCT三維影像與拉曼頻譜之特徵選取 115
5.3.2分析方法與流程 115
5.3.3判別結果 117
第六章 結論與未來展望 134
6.1結論 134
6.2未來展望 137
參考文獻 139

[1]N. Kourkoumelis, I. Balatsoukas, V. Moulia et al., “Advances in the in Vivo Raman Spectroscopy of Malignant Skin Tumors Using Portable Instrumentation,” International Journal of Molecular Science, vol. 16, no. 7, pp. 14554-14570, 2015.
[2]H. W. Rogers, M. A. Weinstock, A. R. Harris et al., “Incidence Estimate of Nonmelanoma Skin Cancer in the United States, 2006,” American Medical Association, vol. 146, no. 3, pp. 283-287, Mar. 2010.
[3]S. E. Hoey, C. E. Devereux, L. Murray et al., “Skin cancer trends in Northern Ireland and consequences for provision of dermatology services,” British Journal of Dermatology, vol. 156, pp. 1301-1307, 2007.
[4]M. R. Donaldson and B. M. Coldiron, “No End in Sight: The Skin Cancer Epidemic Continues,” Seminars in Cutaneous Medicine and Surgery, vol. 30, pp. 3-5, 2011.
[5]J. Sng, D. Koh, W. C. Siong et al., “Skin cancer trends among Asians living in Singapore from 1968 to 2006,” Journal of the American Academy of Dermatology, vol.61, no. 3, pp. 426-432, 2009.
[6]M. C. Simoes, J. J. Sousa and A. A. Pais, “Skin cancer and new treatment perspectives: A review,” Cancer Letters, vol. 357, no. 1, pp. 8-42, 2015
[7]U. Leiter, U. Keim, T. Eigentler et al., “Incidence, Mortality, and Trends of Nonmelanoma Skin Cancer in Germany,” Journal of Investigative Dermatology, vol. 137, pp. 1860-1867, 2017.
[8]K. G. Lewis and M. A. Weinstock, “Trends in Nonmelanoma Skin Cancer Mortality Rates in the United States, 1969 through 2000,” Journal of Investigative Dermatology, vol. 127, pp. 2323-2327, 2007.
[9]“Melanoma: Statistics.” [Online]. Available: https://www.cancer.net/cancer-types/melanoma/statistics
[10]K. Murata and M. Wolf, “Cryo-electron microscopy for structural analysis of dynamic biological macromolecules,” BBA-General Subjects, vol. 1862, pp. 324-334, 2018.
[11]T. Watson, N. Andrews, S. Davis et al., “OPTiM: Optical projection tomography integrated microscope using open-source hardware and software,” PLOS ONE, vol. 12, no. 7, pp. 1-13, 2017.
[12]J. Mayer, A. Robert-Moreno, R. Danuser et al., “OPTiSPIM: integrating optical projection tomography in light sheet microscopy extends specimen characterization to nonfluorescent contrasts,” Optics Letters, vol. 39, no. 4, pp. 1053-1056, 2014.
[13]Y. Liu, A. M. Kiss, D. H. Larsson et al., Spetrochimica Acta Part B: Atomic Spectroscopy. Elsevier, 2016.
[14]E. J. Ngen, L. Wang, Y. Kato et al., “Imaging transplanted stem cells in real time using an MRI dualcontrast method,” Scientific Reports, vol. 5, no. 13628, pp. 1-13, 2015.
[15]E. E. Hoover and J. A. Squier, “Advances in multiphoton microscopy technology,” Nature Photonics, vol. 7, no. 2, pp. 93-101, 2013.
[16]P. Donfack, M. Rehders, K. Brix et al., “Micro Raman spectroscopy for monitoring alterations between human skin keratinocytes HaCaT and their tumorigenic derivatives A5RT3 – toward a Raman characterization of a skin carcinoma model,” Journal of Raman Spectroscopy, vol. 41, pp. 16-26, 2010.
[17]C. A. Patil, H. Kirshnamoorthi, D. L. Ellis et al., “A Clinical Instrument for Combined Raman Spectroscopy-Optical Coherence Tomography of Skin Cancers,” Lasers in Surgery and Medicine, vol. 43, no. 2, pp. 143-151, 2011.
[18]V. P. Zakharov, I. A. Bratchenko, D. N. Artemyev et al., “Comparative analysis of combined spectral and optical tomography methods for detection of skin and lung cancers,” Journal of Biomedical Optics, vol. 20, no. 2, pp. 1-8, 2015.
[19]G. Wang, J. Sun, J. Ma et al., “Sentiment classification: The contribution of ensemble learning,” Decision Support System, vol. 57, pp. 77-93, 2014.
[20]H. R. Arabnia and Q.-N. Tran, Software Tools and Algorithms for Biological Systems. Springer, 2011.
[21]X. Chapeleau, D. Leduc, C. Lupi et al, Measurements using Optic and RF Waves. New Jersey: John Wiley & Sons, Inc., 2013.
[22]M. V. Klein and T. E. Furtak, Optics. Wiley New York, 1990.
[23]W. Drexler and J. G. Fujimoto, Optical Coherence Tomography – technologies and applications. Second edition. Springer, 2008.
[24]C. Burrus and J. Stone, “Single-crystal fiber optical devices: A Nd: YAG fiber laser,” Applied Physics Letters, vol. 26, pp. 318-320, 1975.
[25]王政凱, “摻鈦藍寶石寬頻晶體光纖光源之製備與檢測,” 國立台灣大學光電工程學研究所, 2011.
[26]Y. Dong, G. Zhou, X. Jun et al., “Luminescence studies of Ce:YAG using vacuum ultraviolet synchrotron radiation,” Materials Research Bulletin, vol. 41, no. 10, pp. 1959-1963, 2006.
[27]許婉儀, “Mirau全域式光學同調斷層掃描術應用於體外黑色素細胞及黑色素癌細胞之分析與判別,” 國立台灣大學光電工程學研究所, 2014.
[28]R. K. Wang and V. V. Tuchin, Advanced Biophotonics: Tissue Optical Sectioning. Taylor & Francis, 2013.
[29]H. Ding, J. Q. Lu, W. A. Wooden et al., “Refractive indices of human skin tissues at eight wavelengths and estimated dispersion relations between 300 and 1600 nm,” Physics in Medicine & Biology, vol. 51, no. 6, pp. 1479, 2006.
[30]“Optical constants of Fused silica.” [Online]. Available: https://refractiveindex.info/?shelf=glass&book=fused_silica&page=Malitson
[31]“The Rayleigh Criterion.” [Online]. Available: http://hyperphysics.phy-astr.gsu.edu/hbase/phyopt/Raylei.html
[32]A. Dubois, L. Vabre, A. C. Boccara et al., “High-resolution full-field optical coherence tomography with a Linnik microscope,” Applied Optics, vol. 41, no. 4, pp. 805-812, 2002.
[33]“Optical resolution.” [Online]. Available: https://en.wikipedia.org/wiki/Optical_resolution#Sensor_resolution_.28spatial.29.
[34]“Camera Resolution.” [Online]. Available: https://www.photonics.com/a29926/Camera_Resolution_Combining_Detector_and_Optics
[35]吳東憶, “高解析且高深度Mirau全域式光學同調斷層掃描之活體皮膚量測,” 國立台灣大學光電工程學研究所, 2015.
[36]“C.V. Raman and the Raman Effect.” [Online]. Available: https://www.acs.org/content/acs/en/education/whatischemistry/landmarks/ramaneffect.html
[37]S. H. Lee, S. -M. Park and P. L. Lee, Optical Methods in Studies of Olfactory System. Springer, 2014.
[38]“The Fermi–Dirac Distribution.” [Online]. Available: https://www.doitpoms.ac.uk/tlplib/semiconductors/fermi.php
[39]N. Tarcea, T. Frosch, P. Rosch et al., “Raman Spectroscopy—A Powerful Tool for in situ Planetary Science,” Space Science Reviews, vol. 135, pp. 281-292, 2008.
[40]E. J. Woodbury and W. K. Ng, “Ruby laser operation in the near IR,” Proceedings of the Institute of Radio Engineers, vol. 50, pp. 2367, 1962.
[41]V. V. Yakovlev, G. I. Petrov, H. F. Zhang et al., “Stimulated Raman scattering: Old physics, new applications,” Journal of Modern Optics, vol. 56, no. 18–19, pp. 1970–1973, 2009.
[42]W. J. Tipping, M. Lee, A. Serrels et al., “Stimulated Raman scattering microscopy: an emerging tool for drug discovery,” Chemical Society Reviews, vol. 45, pp. 2075-2089, 2016.
[43]“Coherent Raman Imaging.” [Online]. Available: https://bernstein.harvard.edu/research/cars-why.htm
[44]“Molecular structure of Glycerol.” [Online]. Available: https://cn.depositphotos.com/202204840/stock-illustration-glycerol-glycerine-glycerin-structure-molecule.html
[45]A. Mudalige and J. E. Pemberton, “Raman spectroscopy of glycerol/D2O solutions,” Vibrational Spectroscopy, vol. 45, no. 1, pp. 27–35, 2007.
[46]M. S. Wrobel, A. P. Popov, A. V. Bykov, “Nanoparticle-free tissue-mimicking phantoms with intrinsic scattering,” Biomedical Optics Express, vol. 7, no. 6, pp. 1-7, 2016.
[47]“Selecting an Excitation Wavelength for RS.” [Online]. Available: http://www.spectroscopyonline.com/selecting-excitation-wavelength-raman-spectroscopy?pageID=4
[48]C. Carteret and A. Labrosse, “Vibrational properties of polysiloxanes: from dimer to oligomers and polymers.1. Structural and vibrational properties of hexamethyldisiloxane (CH3)3SiOSi(CH3)3,” Journal of Raman Spectroscopy, vol. 41, pp. 996-1004, 2010.
[49]P. C. Ashok, G. P. Singh and H. A. Rendall, “Waveguide confined Raman spectroscopy for microfluidic interrogation,” The Royal Society of Chemistry, vol. 11, pp. 1262-1270, 2011.
[50]“Anatomy of the Skin.” [Online]. Available: https://training.seer.cancer.gov/melanoma/anatomy/
[51]H. O. Alsaab, “Evaluation of the Percutaneous Absorption of Chlorpromazine Hydrochloride from PLO Gels Across Porcine Ear and Human Abdominal Skin,” The University of Toledo, 2015.
[52]“認識皮膚的生理結構.” [Online]. Available: http://drtonywu.pixnet.net/blog/post/26010100-【醫師專欄-皮膚】
[53]“皮膚生理構造.” [Online]. Available: http://mikevgh.pixnet.net/blog/post/25185686
[54]“Dermis.” [Online]. Available: https://en.wikipedia.org/wiki/Dermis
[55]P. Ramasastry, D. T. Downing, P. E. Pochi et al., “Chemical Composition of Human Skin Surface Lipids from Birth to Puberty,” The Journal of Investigative Dermatology, vol. 54, no. 2, pp. 139-144, 1970.
[56]“癸酸三酸甘油酯(Caprylic / Capric Triglyceride).” [Online]. Available: http://maggiemuses35c.pixnet.net/blog/post/382107718
[57]M. Jaworska, E. Sikora and J. Ogonowski, “The influence of glicerides oil phase on O/W nanoemulsion formation by pic method,” Periodica Polytechnica – Chemical Engineering, vol. 58, pp. 43-48, 2014.
[58]S. Bresson, M. El Marssi and B. Khelifa, “Raman spectroscopy investigation of various saturated monoacid triglycerides,” Chemistry and Physics of Lipids, vol. 134, pp. 119-129, 2005.
[59]“快速搞懂角鯊烷(Squalane)？角鯊烯(Squalene).” [Online]. Available: http://bonniecute0322.pixnet.net/blog/post/345354167
[60]“角鯊烯，有這麼厲害嗎？.” [Online]. Available: http://b303094004.pixnet.net/blog/post/291251851
[61]“Squalene.” [Online]. Availble: https://www.sigmaaldrich.com/catalog/product/mm/821068?lang=en®ion=TW
[62]H. J. Chun, T. L. Weiss, T. P. Devarenne et al., “Vibrational spectra and DFT calculations of squalene,” Journal of Molecular Structure, vol. 1032, pp. 203-206, 2013.
[63]“Squalane.” [Online]. Availble: http://www.chemspider.com/Chemical-Structure.7798.html
[64]K. N. Collier and S. D. Brooks, “Role of Organic Hydrocarbons in Atmospheric Ice Formation via Contact Freezing,” The Journal of Physical Chemistry A, vol. 120, pp. 10169-10190, 2016.
[65]“Cancer Stat Facts: Melanoma of the Skin.” [Online]. Availble: https://seer.cancer.gov/statfacts/html/melan.html
[66]GBD 2015 Disease and Injury Incidence and Prevalence Collaborators, “Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015,” The Lancet, vol. 388, pp. 1545-1602, 2016.
[67]A. Levine, K. Wang and O. Markowitz, “Optical Coherence Tomography in the Diagnosis of Skin Cancer,” Dermatologic Clinics, vol. 35, pp. 465-488, 2017.
[68]C. Curiel-Lewandrowski, C. M. Williams, K. J. Swindells et al., “Use of In Vivo Confocal Microscopy in Malignant Melanoma,” JAMA Dermatology, vol. 140, no. 9, pp. 1127-1132, 2004.
[69]F. Farnetani, A. Scope, R. P. Braun et al., “Skin Cancer Diagnosis With Reflectance Confocal Microscopy: Reproducibility of Feature Recognition and Accuracy of Diagnosis,” JAMA Dermatology, vol.151, no. 10, pp. 1075-1080, 2015.
[70]K. M. Vincent and L. –M. Postovit, “Investigating the utility of human melanoma cell lines as tumour models,” Oncotarget, vol. 8, no. 6, pp. 10498-10509, 2017.
[71]“Basal Cell Carcinoma (BCC).” [Online]. Availble: https://www.skincancer.org/skin-cancer-information/basal-cell-carcinoma
[72]“Basal-cell carcinoma.” [Online]. Availble: https://en.wikipedia.org/wiki/Basal-cell_carcinoma
[73]“Squamous cell skin cancer.” [Online]. Availble:
https://en.wikipedia.org/wiki/Squamous_cell_skin_cancer
[74]P. Boukamp, R. T. Petrussevska, D. Breitkreutz et al., “Normal Keratinization in a Spontaneously Immortalized Aneuploid Human Keratinocyte Cell Line,” The Journal of Cell Biology, vol. 106, pp. 761-771, 1988.
[75]A. F. Deyrieux and V. G. Wilson, “In vitro culture conditions to study keratinocyte differentiation using the HaCaT cell line,” Cytotechnology, vol. 54, pp. 77-83, 2007.
[76]L. –C. Chiang, W. Chiang, H. –S. Yu et al., “Establishment and Characterization of a Continuous Human Basal Cell Carcinoma Cell Line from Facial Skin (I) Cytological Behavior of Early Passages,” The Kaohsiung Journal of Medical Sciences, vol. 10, no. 4, pp. 170-176, 1994.
[77]“Melanoma Pathology.” [Online]. Availble: http://www.melanoma.org/understand-melanoma/diagnosing-melanoma/melanoma-pathology
[78]L. Hayflick and P. S. Moorhead, “The serial cultivation of human diploid cell strains,” Experimental Cell Research, vol. 25, no. 3, pp. 585–621, 1961.
[79]“Extracellular Matrix.” [Online]. Availble: https://biologydictionary.net/extracellular-matrix/
[80]H. Wang, T. H. Tsai, J. Zhao et al., “Differentiation of HaCaT cell and melanocyte from their malignant counterparts using micro-Raman spectroscopy guided by confocal imaging,” Photodermatology, Photoimmunology and Photomedicine, vol. 28, pp. 147–152, 2012.
[81]張家凱, “全域式光學同調斷層掃描儀於皮膚組織之量化參數研究,” 國立台灣大學光電工程學研究所, 2018.
[82]A. N. Bashkatov, E. A. Genina, V. I. Kochubey et al., “Optical properties of melanin in the skin and skinlike phantoms,” Proceedings of SPIE, vol. 4162, pp. 219-226, 2000.
[83]J. M. Prewitt and M. L. Mendelsohn, “The analysis of Cell Images,” Annals of the New York Academy of Sciences, vol. 128, no. 3, pp. 1035-1053, 1966.
[84]T. W. Ridler and S. Calvard, “Picture Thresholding Using an Iterative Selection Method,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 8, no. 8, 1978.
[85]“Auto Threshold.” [Online]. Availble: https://imagej.net/Auto_Threshold
[86]C. A. Glasbey, “An analysis of histogram-based thresholding algorithms,” CVGIP: Graphical Models and Image Processing, vol. 55, no. 6, pp. 532-537, 1993.
[87]“Watershed segmentation.” [Online]. Availble: https://svi.nl/watershed
[88]N. –C. Cheng, T. –H. Hsieh, Y. –T. Wang et al., “Cell death detection by quantitative three-dimensional single-cell tomography,” Biomedical Optics Express, vol. 3, no. 9, 2012.
[89]Y. Barrandon and H. Green, “Cell size as a determinant of the clone-forming ability of human keratinocytes,” PNAS, vol. 82, no. 16, 1985.
[90]F. O. Nestle, P. D. Meglio, J. –Z. Qin et al., “Skin immune sentinels in health and disease,” Nature Reviews Immunology, vol. 9, no. 10, pp. 679-691, 2009.
[91]謝宗勳, “超高解析光學同調斷層掃描於單顆皮膚細胞之造影與分析,” 國立台灣大學光電工程學研究所, 2012.
[92]L. Wiemerslage and D. Lee, “Quantification of Mitochondrial Morphology in Neurites of Dopaminergic Neurons using Multiple Parameters,” Journal of Neuroscience Methods, vol. 262, pp. 56-65, 2016.
[93]B. Beauvoit, T. Kitai and B. Chance, “Contribution of the mitochondrial compartment to the optical properties of the rat liver: a theoretical and practical approach,” Biophysical Journal, vol. 67, pp. 2501-2510, 1994.
[94]C. Sammut and G. I. Webb, Encyclopedia of Machine Learning. Springer, 2010.
[95]J. Deng, A. C. Berg, K. Li et al., “What Does Classifying More Than 10,000 Image
Categories Tell Us?,” ECCV 2010, Part V, LNCS 6315, pp. 71-84, 2010.
[96]S. P. Singh, S. Janjuha, S. Chaudhuri et al., “Machine learning based classification of cells into chronological stages using single-cell transcriptomics,” bioRxiv, pp. 1-45, 2018.
[97]P. K. Douglas, S. Harris, A. Yuille et al., “Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief,” NeuroImage, vol. 56, no. 2, pp. 544-553, 2011.
[98]J. Buhagiar, “Automatic Segmentation of Indoor and Outdoor scenes from Visual Lifelogging,” The University of Malta, 2017.
[99]“Bagging – building an ensemble of classifiers from bootstrap samples.” [Online]. Availble: https://www.packtpub.com/mapt/book/big_data_and_business_intelligence/9781783555130/7/ch07lvl1sec46/bagging--building-an-ensemble-of-classifiers-from-bootstrap-samples
[100]“R筆記 – (16) Ensemble Learning(集成學習).” [Online]. Availble: https://rpubs.com/skydome20/R-Note16-Ensemble_Learning
[101]C. M. Bishop, Pattern Recognition And Machine Learning. Springer, 2006.
[102]T. M. Cover and P. E. Hart, “Nearest Neighbor Pattern Classification,” IEEE Transactions on Information Theory, vol. 13, no. 1, pp. 21-27, 1967.
[103]“最近鄰居法.” [Online]. Availble: https://zh.wikipedia.org/wiki/%E6%9C%80%E8%BF%91%E9%84%B0%E5%B1%85%E6%B3%95
[104]B. P. Hall, B. U. Park and R. J. Samworth, “CHOICE OF NEIGHBOR ORDER IN NEAREST-NEIGHBOR CLASSIFICATION,” The Annals of Statistics, vol. 36, no. 5, pp. 2135-2152, 2008.
[105]“Decision Trees: A Disastrous Tutorial.” [Online]. Availble: https://www.kdnuggets.com/2016/09/decision-trees-disastrous-overview.html
[106]“Introduction To Entropy.” [Online]. Availble: http://matlabdatamining.blogspot.com/2006/11/introduction-to-entropy.html
[107]G. Hastings, R. Wang, P. Krug et al., “Infrared microscopy for the study of biological cell monolayers. I. Spectral effects of acetone and formalin fixation,” Biopolymers, vol. 89, no. 11, pp. 921-930, 2008.
[108]E. Gazi, J. Dwyer, N.P. Lockyer et al., “Fixation protocols for subcellular imaging by synchrotron-based Fourier transform infrared microspectroscopy,” Biopolymers, vol. 77, no. 1, pp. 18-30, 2005.
[109]J. W. Chan, D. S. Taylor and D. L. Thompson, “The effect of cell fixation on the discrimination of normal and leukemia cells with laser tweezers Raman spectroscopy,” Biopolymers, vol. 91, no. 2, pp. 132-139, 2009.
[110]N. Kourkoumelis, I. Balatsoukas, V. Moulia et al., “Advances in the in Vivo Raman Spectroscopy of Malignant Skin Tumors Using Portable Instrumentation,” International Journal of Molecular Science, vol. 16, no. 7, pp. 14554-14570, 2015.
[111]X. Feng, A. J. Moy, H. T. Nguyen et al., “Raman active components of skin cancer,” Biomedical Optics Express, vol. 8, no. 6, pp. 2835-2850, 2017.
[112]J. Martı´-Rujas, K. D. Harris, A. Desmedt et al., “Significant Conformational Changes Associated with Molecular Transport in a Crystalline Solid,” The Journal of Physical Chemistry B, vol. 110, no. 22, pp. 10708-10713, 2006.
[113]Y. Chen, J. Dai, X. Zhou et al., “Raman Spectroscopy Analysis of the Biochemical Characteristics of Molecules Associated with the Malignant Transformation of Gastric Mucosa,” PLOS ONE, vol. 9, no. 4, pp. 1-11, 2014.
[114]“Raman: Wavelength Matters.” [Online]. Availble: https://wasatchphotonics.com/technologies/raman-spectroscopy-wavelength-matters/
[115]“The effect of cooling CCD detectors for spectroscopy.” [Online]. Availble: https://ibsen.com/technology/detector-tutorial/the-effect-of-cooling-ccd-detectors-for-spectroscopy/
[116]X. Feng, A. J. Moy, H. T. Nguyen et al., “In vivo Raman Spectroscopic Sensing of Biophysical Changes in Skin Cancer,” CLEO 2017, ATu4A.2, pp. 1-2, 2017.
[117]C. A. Lieber and A. Mahadevan-Jansen, “Automated Method for Subtraction of Fluorescence from Biological Raman Spectra,” Applied Spectroscopy, vol. 57, no. 11, pp. 1363-1367, 2003.
[118]C. –H. Liu, B. Wu, S. Boydston-White et al., “Characterization and discrimination of basal cell carcinoma and normal human skin tissues using resonance Raman spectroscopy,” Frontiers in Optics 2017, JTu2A.72, pp. 1-2, 2017.
[119]M. Gniadecka, H. C. Wulf, N. N. Mortensen et al., “Diagnosis of Basal Cell Carcinoma by Raman Spectroscopy,” Journal of Raman Spectroscopy, vol. 28, no. 2-3, pp. 125-129, 1997.
[120]S. T. Ishikawa and V. C. Gulick, “An automated mineral classifier using Raman spectra,” Computers and Geosciences, vol. 54, pp. 259-268, 2013.