請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/72267
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 黃升龍(Sheng-Lung Huang) | |
dc.contributor.author | Dan Ji | en |
dc.contributor.author | 季丹 | zh_TW |
dc.date.accessioned | 2021-06-17T06:32:17Z | - |
dc.date.available | 2018-08-21 | |
dc.date.copyright | 2018-08-21 | |
dc.date.issued | 2018 | |
dc.date.submitted | 2018-08-16 | |
dc.identifier.citation | [1] W. Drexler, J. G. Fujimoto, “Optical Coherence Tomography-technologies and applications,” Second edition
[2] A. F. Fercher, “Ophthalmic interferometry,” Proceedings of the International Conference on Optics in Life Sciences (1990) [3] D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, “Optical coherence tomography,” Science, 254, 1178-1181(1991) [4] A. F. Fercher, C. K. Hitzenberger, W. Drexler, “In vivo optical coherence tomography,” Am. J. Ophthalmol, 116, 113-114(1993) [5] J. M. Schmitt, A. Knuttel, M. Yadlowsky, “Subsurface imaging of living skin with optical coherence tomography,” Dermatol, vol. 191, 93-95(1995) [6] J. G. Fujimoto, M. E. Brezinski, G. J. Tearney, “Optical biopsy and imaging using optical coherence tomography,” Nature Med., vol. 1, 970-972(1995) [7] J. G. Fujimoto, S. A. Boppart, G. J. Tearney, B. E. Bounma, “High resolution in vivo intra-arterial imaging with optical coherence tomography,” Heart, 82.2:128-133 (1999) [8] E. Beaurepaire, A. C. Boccara, M. Lebec, “Full-field optical coherence microscopy,” Optics Letters, 23.4 (1998) [9] M. Wojtkowski, R. Leitgeb, A. Kowalczyk, “In vivo human retinal imaging by Fourier domain optical coherence tomography,” J. Biomed. Opt. 7:457-463 (2002) [10] A. Dubois, L. Vabre, A. C. Boccara, et al. “High-resolution full-field optical coherence tomography with a Linnik microscope,” Applied optics, 41.4: 805-812 (2002) [11] D. Zhang, X. Jing, J. Yang, “Biometric Image Discrimination Technologies,” Idea Group Pub (2006) [12] 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瘤細胞之分析與判別”國立台灣大學光電工程學研究所,2014 [13] 簡孟庭,“高解析度Mirau全域式光學同調斷層掃描儀於組織切片之腫瘤辨識研究”國立台灣大學光電工程研究所,2015 [14] K. Pearson, “On Lines and Planes of Closest Fit to Systems of Points in Space,” Philosophical Magazine, 2.6:556-572 (1901) [15] https://www.cnblogs.com/pinard/p/6244265.html [16] https://blog.csdn.net/xlinsist/article/details/51311755 [17] https://ratsgo.github.io/machine%20learning/2017/05/30/SVM3/ [18] https://seer.cancer.gov/statfacts/html/melan.html [19] A. Esteva, et al. 'Dermatologist-level classification of skin cancer with deep neural networks,' Nature 542.7639 (2017) [20] Catherine St-Pierre, et al. 'Dimension reduction technique using a multilayered descriptor for high-precision classification of ovarian cancer tissue using optical coherence tomography: a feasibility study,' Journal of Medical Imaging 4.4 (2017) [21] Linda Shapiro. 'Computer vision,' (2000). [22] http://dataunion.org/13451.html [23] https://www1.cgmh.org.tw/intr/intr2/c3280/view_page.asp?v_id=83 [24] https://www.hpa.gov.tw/Pages/Detail.aspx?nodeid=615&pid=1126 [25] http://crshsieh.blogspot.com/2016/10/crcstage.html [26] https://www.careonline.com.tw/2017/07/colon-ca-stage.html [27] N. Bagri, P. K. Johari, 'A comparative study on feature extraction using texture and shape for content based image retrieval,' International Journal of Advanced Science and Technology 80.4:41-52 (2015) [28] R. M. Haralick, K. Shanmugam, I. H. Dinstein, 'Textural features for image classification,' IEEE Transactions on Systems, Man, and Cybernetics 3.6: 610-621 (1973) [29] http://www.math.nsysu.edu.tw/~lomn/homepage/R/R_testing.htm [30] C. Cortes, V. Vapnik, 'Support-vector networks,' Machine Learning 20.3:273-297 (1995) | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/72267 | - |
dc.description.abstract | 光學同調斷層掃描(OCT)具有非侵入性、樣本無需標定及空間解析度佳等特性,是現今生醫領域重要的三維成像技術之一,因為其特有優勢在眼科與心血管科已發揮著重要作用。但對於皮膚科以及腸胃科而言,不管是皮膚癌還是大腸癌的診斷,醫師目前採取的方式均為切片檢查的方式,這些均屬於侵入式檢查。對於皮膚而言,會讓病患流血且可能留下疤痕影響美觀。而對於大腸而言,增加切片量即會增加腸道出血甚至穿孔的機會。不管是皮膚癌還是大腸癌,醫師均需藉助病理切片檢查癌症區域是否有切除乾淨,這意味著病人有可能會需要二次甚至多次手術。
本論文所用之Mirau全域式光學同調斷層掃描儀(Mirau-based FF-OCT)具有高橫向解析度(0.8 μm)與縱向解析度(0.9 μm)之特點,其光源為實驗室自行生長之摻鈰釔鋁石榴石晶體光纖自發輻射(spontaneous emission; SE)。實驗分兩個獨立部分進行。其一為利用FF-OCT系統配合大面積掃描平台,掃描24組非癌症皮膚與黑色素細胞癌之空白組織切片,得到24組非癌症與癌症之三維OCT對比影像。其後根據醫師所圈選之Ground truth選取部分區域作為訓練集與測試集,並提取其有效橫向與縱向特徵共計19個,利用訓練集所建立之判別模型,對測試集進行癌症區域與非癌症區域的判別分析,橫向判別解析度為48 μm*54 μm(108 pixel*122 pixel),判別演算法為線性判別分析(LDA),判別正確率為87.5%。其二為同樣利用FF-OCT大面積掃描系統掃描得到16組非癌症大腸與大腸癌之三維OCT對比影像,其後根據醫師所判斷之Ground truth,對有組織區域提取其有效橫向特徵與縱向特徵共計22個參數,進行癌症區域與非癌症區域的判別分析,橫向判別解析度為222 μm*222 μm(500 pixel*500 pixel),判別演算法為支持向量機(SVM),判別正確率為87.4%。大腸癌之最佳判別解析度遠大於黑色素細胞癌的原因與所提取橫向特徵的特性以及大腸癌及非癌症大腸之樣本所具有的結構有關。 本論文分別展現了兩套演算法配合FF-OCT系統之於非活體黑色素細胞癌與大腸癌診斷之潛力,為OCT系統後續應用于活體皮膚癌之偵測與大腸癌之偵測提供一個前期研究之參考。 | zh_TW |
dc.description.abstract | Optical coherence tomography (OCT) is an important three-dimensional optical imaging technique in biomedical realm. It has non-invasive, label-free, and high spatial resolution characteristics. Based on its unique advantages, OCT plays a crucial role in clinical diagnosis in ophthalmology and cardiovascular system. However, in dermatology and gastroenterology, the clinical diagnosis for skin cancer and colon cancer still relies on biopsy, which is an invasive procedure. Biopsy in skin will cause bleeding and may leave scars. For diagnosis of colon cancer, increasing the amount of biopsy will increase the chance of bleading and even the possibility of perforation of the large intestine. Besides, whether it is skin cancer or colon cancer, doctors need to rely on biopsy to check whether the tumor area is removed thoroughly, which means that patients may need second or multiple operations.
The Mirau-based OCT system used in this thesis has high lateral resolution of 0.8 μm and axial resolution of 0.9 μm. A homemade 〖Ce〗^(3+):YAG crystal fiber spontaneous emission (SE) light source was used to build the FF-OCT system. The experiments were conducted in two separate parts: melanoma part and colon cancer part. In the melanoma part, FF-OCT with a XY stage was used to scan 24 sets blank tissue sections. A set includes OCT images of normal skin tissue and melanoma tissue. Based on the ground truth provided by a dermatologist, regions were chosen to be training set and testing set. Nineteen features of lateral and vertical were extracted from these regions. The discriminant model is established by 19 features extracted from the training set, and the discriminant model was applied to the testing set to distinguish normal tissue and melanoma tissue. The lateral discriminant resolution is 48 μm*54 μm (108 pixel *122 pixel), and the discriminant algorithm is linear discriminant analysis (LDA). The mean discriminant accuracy in 24 sets OCT images is 87.5%. In the colon cancer part, FF-OCT with a XY stage was used to scan 16 sets unstained tissue sections which include normal large intestine tissue and colon cancer tissue. Based on the ground truth provided by a pathologist, 22 features were extracted from regions including tissues in OCT images. Based on the discriminant model established by 22 features extracted from the training set, the mean discriminant accuracy in the 16 sets OCT images is 87.4%. The lateral discriminant resolution is 222 μm*222 μm (500 pixel *500 pixel), and the discriminant method is support vector machine (SVM). The best discriminant resolution of colon cancer is much larger than Melanoma. It is related to the characteristics of the extracted lateral features and the periodic structure of large intestine tissues. This thesis presents one algorithm for discrimination of OCT images of melanoma and normal skin tissue, another for discrimination of OCT images of colon cancer and normal large intestine tissue. This study provides a preliminary study for the potentially possible applications of OCT in skin cancer and colon cancer diagnosis in vivo. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T06:32:17Z (GMT). No. of bitstreams: 1 ntu-107-R03941107-1.pdf: 30986113 bytes, checksum: c7f9d9ff872617b8d0d02102376caec6 (MD5) Previous issue date: 2018 | en |
dc.description.tableofcontents | 中文摘要 I
ABSTRACT III 目錄 VI 圖目錄 IX 表目錄 XIV 第一章 緒論 1 1.1 背景簡介 1 1.2 研究動機與目的 2 1.3 本文概要 4 第二章 基本原理及系統架構 5 2.1 光學低同調干涉術 5 2.1.1 TD-OCT基本原理 5 2.1.2 FF-OCT基本原理及空間解析度 9 2.2 系統架構及特性 10 2.2.1 光源 10 2.2.2 系統架構 13 2.2.3 系統解析度 14 2.3 傳統組織病理切片之製備流程 15 2.4 電腦輔助診斷之演算法 17 2.4.1 主成分分析 18 2.4.2 線性判別分析 20 2.4.3 二次判別分析 22 2.4.4 支持向量機 22 第三章 組織切片製備掃描比對之研究 27 3.1組織病理切片之製備 27 3.2影像拼接 30 3.2.1組織切片之OCT影像拼接及Mosaic消除 30 3.2.2 H&E染色切片之OM影像拼接及Mosaic消除 34 3.3 樣本掃描及比對-Ground truth之建構 35 第四章 電腦輔助診斷黑色素細胞癌之研究 40 4.1 研究動機與研究現狀 40 4.2 樣本數目及判別之訓練集及測試集之選取 40 4.3 特徵參數之提取 45 4.4 特徵影像 56 4.5特徵參數提取對大面積鑑別之有效性評估 72 4.6 不同演算法對大面積鑑別之有效性評估 84 4.7 本章小結 96 第五章 電腦輔助大腸癌之研究 97 5.1 大腸癌介紹 97 5.2 樣本數目及判別之訓練集與測試集選取 99 5.3 特徵參數介紹 104 5.4 特徵參數之組合優化及對大面積鑑別之有效性評估 109 5.5 不同橫向判別解析度對判別結果之影響 146 5.6 黑色素皮膚癌與大腸癌之演算法互換測試結果 147 5.7 病理科醫師對大腸癌之OCT影像盲測結果 149 5.8 本章小結 150 第六章 結論與展望 152 6.1 結論 152 6.2 未來展望 153 參考文獻 154 附錄一 黑色素細胞癌HE與OCT比對影像 157 附錄二 大腸癌HE與OCT比對影像 181 | |
dc.language.iso | zh-TW | |
dc.title | Mirau 全域式光學同調斷層掃描術應用於黑色素細胞癌與大腸癌之電腦輔助診斷 | zh_TW |
dc.title | Computer-Aided Diagnosis of Melanoma and Colon
Cancer Utilizing Mirau-Based Full-Field Optical Coherence Tomography | en |
dc.type | Thesis | |
dc.date.schoolyear | 106-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 孫家棟,邱政偉(Jeng-Wei Tjiu),李昱儀,廖肇裕 | |
dc.subject.keyword | Mirau全域式光學同調斷層掃描,黑色素細胞癌,大腸癌,特徵提取,線性判別分析(LDA),支持向量機(SVM), | zh_TW |
dc.subject.keyword | Mirau-based full-field optical coherence tomography,melanoma,colon cancer,feature extraction,linear discriminant analysis (LDA),support vector machine (SVM), | en |
dc.relation.page | 196 | |
dc.identifier.doi | 10.6342/NTU201803631 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2018-08-16 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 光電工程學研究所 | zh_TW |
顯示於系所單位: | 光電工程學研究所 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-107-1.pdf 目前未授權公開取用 | 30.26 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。