臺灣博碩士論文加值系統

肝母細胞癌在分化時有胚胎肝細胞分化、胎兒肝細胞分化、間質組織分化及上皮組織分化等四類。但胎兒肝細胞分化型與肝細胞癌形態相似，故本研究使用影像處理技術處理17個肝母細胞癌與20個肝細胞癌，並且計算細胞核形態參數如核質比、細胞核平均面積與平均直徑、細胞平均面積、細胞核面積標準差、細胞核最大面積、細胞核面積最大與最小的差值、細胞核直徑標準差、細胞核最大直徑、細胞核直徑最大與最小的差值，最後使用這些參數計進行t檢定及多變量判別方法分析。
由本研究分析結果發現肝母細胞瘤胎兒型與肝細胞癌相比，前者有較高的核質比(P=0.034)、細胞較小(P=0.022)並且細胞核的面積與直徑大小較一致 (包括核面積標準差(P<0.001)、核直徑標準差(P<0.001)、最大核面積(P=0.039)、最大核直徑(P=0.031)、核面積最大與最小值之差值(P=0.009)和核直徑最大與最小值之差值(P<0.001)都較小)。利用多變量判別方法分析，發現可以藉由核質比、細胞平均面積、細胞核面積標準差及細胞核直徑標準差等四個參數值，對於區分肝細胞癌與肝母細胞瘤胎兒型達到91.6%正確判別率。其結果可輔助病理科醫師較準確判別出腫瘤類型，以利病人後續治療。

Hepatoblastomas display a distinct variety of patterns, including embryonal epithelial differentiation, fetal epithelial differentiation, mesenchymal differentiation, teratoid differentiation. The differential diagnosis between fetal epithelial differentiation of hepatoblastoma and hepatocelllur carcinoma may be difficult because both have similar differentiation toward normal hepatocytes. In this study, we use image processing technology to calculate the nucleus/cytoplasm ratio, the mean of the nuclear area or diameter, the mean of the cellular area, the standard deviation of nuclear area or diameter, the maximum of the nuclear area or diameter, and the range of the nuclear area or diameter in 17 hepatoblastomas and 20 hepatocelluar carcinomas.
The cells of fetal epithelial differentiation, compared to those of hepatocellular carcinoma, have higher nucleus/cytoplasm ratio (P=0.034), smaller cellular area (P=0.022) and more uniform nuclei (P<0.001, P<0.001, P=0.039, P=0.031, P=0.009 and P<0.001 for the standard deviation of nuclear area, the standard deviation of nuclear diameter, the maximum of the nuclear area, the maximum of the nuclear diameter, the range of the nuclear area and the range of the nuclear diameter, respectively). Multivariate classification analysis showed that 91.6% of cases could be classified correctly by using four parameters, including the nucleus/cytoplasm ratio, the mean of the cellular area, the standard deviation of nuclear area and the standard deviation of nuclear diameter. The results indicated that the image processing technology could be used as an assisting examination to distinguish between these tumors.

摘要……………………………………………………………………. I
Abstract……………………………………………………………… II
誌謝……………………………………………………………………… IV
目錄……………………………………………………………………… V
表目錄……………………………………………………………….. VII
圖目錄………………………………………………………………….. VIII
符號索引…………….. …………………………………………………. X
第1章 前言………………………………………………………………….1
1.1 研究背景與動機…………………………………………………...1
1.2 文獻回顧…………………………………………………………...2
1.3 研究目的…………………………………………………………...4
1.4 本文架構…………………………………………………………4
第2章 理論分析…………………………………………………………….6
2.1 數位影像表示……………………………………………………...6
2.2 色彩表示模型……………………………………………...……..7
2.2.1 RGB色彩模型……………..................................................7
2.2.2 HIS色彩模型..…………………………………………8
2.3 影像處理…………………………………………………….……10
2.3.1 中值濾波處理..………………………………………….10
2.3.2 像素關係與運算…………………………………………11
2.3.3 最佳閥值選定…………………………………………...13
2.4 形態學影像處理………………………………………………….14
2.4.1 侵蝕與膨脹………………………………………….……14
2.4.2 邊界抽取………………………………………………….15
2.4.3 區域填充………………………………………………….16
2.4.4 物體標示與識別……………………………………….…17
2.5 物體直徑計算………………………………….………………..18
2.6 統計分析方法………………………………………………….…18
第3章 研究設備及步驟……………...……………………………………20
3.1 研究材料與設備……………………………………………….....20
3.2 影像處理步驟…………………………………………………….20
3.3 影像前處理………………………………………………….……21
3.3.1 色彩轉換………………………..………………………….21
3.3.2 雜訊去除……………………………………………..…….23
3.4 最佳閥值選定與二值化………………………………………….23
3.5 細胞核修正…………………………………………………….…23
3.6 細胞核與其他區域選取………………………………….………23
3.6.1 單一細胞核選取…………………………...…………..…..24
3.6.2 其他部分選取…………………………………………….24
3.7 各參數計算與分析…………………………...…………………..25
第4章 結果與討論……………………………………………….………..27
4.1 程式驗證…………………………………………………………….27
4.2 影像處理…………………………………………………………….30
4.2.1 形態學處理…………...………………………………..…..31
4.2.1.1 侵蝕與膨脹………………………….………….31
4.2.1.2 區域填充………………………………….…….33
4.2.2 單一細胞核擷取……………………….……..………….33
4.3 細胞核直徑……………...……………………………………..……34
4.4 細胞質選取……………………………………………………….....35
4.5 統計分析歸納……………………………………….………………36
第5章 結論…………….…………………………………………………..50
5.1 結論………………………………………………………………….50
5.2 未來方向………………………………………………….………...50
參考文獻…………………………………………………………………….51
作者簡介……………………………………………………………………54

參考文獻
1.阮秋琦（2004）數位圖像處理(MATLAB版)。電子工業出版社，第361頁。
2.胡小鋒、趙輝 (2004）Visual C++/MATLAB圖像處理與識別實用案列精選。人民郵電出版社，第190頁。
3.陳逸雯（2002）肺部腫瘤偵測之電腦輔助診斷系統。中原大學醫學工程學系碩士論文。
4.陳靜宜（2005）影像處理及類神經網路於微細胞核自動計數之應用。元智大學資訊管理學系碩士論文。
5.黃維仕（2005）多分光影像於蝴蝶蘭瓶苗生物性污染之檢測。國立屏東科技大學機械工程系碩士論文。
6.程慧娟（2003）圖解醫學統計學。合記圖書出版社，第18-19頁。
7.劉于綸（2004）細胞顯微影像分割與運動分析。國立中央大學機械系研究所碩士論文。
8.數位新知（2005）統計學實務：活用Excel應用與分析。金禾資訊股份有限公司，第7-23至7-30頁。
9.繆紹綱（2003a）數位影像處理-活用Matlab。全華科技圖書股份有限公司。
10.繆紹綱（2003b）數位影像處理。普林斯頓國際有限公司。
11.攜手面對肝細胞癌（2006）肝細胞癌醫療團隊製作。國立成功大學醫學院附設醫院，第7頁。
12.Gonzales-Barron Ursula, and Francis Butler (2006) A comparison of seven thresholding techniques with the k-means clustering algorithm for measurement of bread-crumb feature by digital image analysis. JOURNAL OF FOOD ENGINEERING 74:268-278.
13.Hytiroglou Prodromos, Noam Harpaz, Debra S. Heller, Zhiyuan Liu, Liane Deligdisch, and Joan Gil (1992) Differential Diagnosis of Borderline and Invasive Serous Cystadenocarcinomas of the Ovary by Computerized Interactive Morphometric Analysis of Nuclear Features. Cancer 69:988-992.
14.Kamoi Seiryu, Yoshiharu Ohaki, Yasuo Amano, Osamu Mori, Norihiro Matsushita, Daiske Doi, Susumu Okada, Yuuichi Sugisaki, Takashi Kawamura, and Tsutomu Araki (2003) Pre-treatment Mitotic Index Versus Computer-quantitated Ki-67 Nuclear Antigen Labeling Index as Predictors of Response to Neoadjuvant Chemotherapy in Uterine Cervical Carcinoma. J Nippon Med Sch 70(3):219-226.
15.Kim Y.-J., B.F.M. Romeike, J. Uszkoreit, and W. Feiden (2006) Automated nuclear segmentation in the determination of the Ki-67 labeling index in meningiomas. Clinical Neuropathology, Vol.25:67-73.
16.Lazaros G.A., K.S. Stefanaki, I.G. Panayiotides, M.N. Tzardi, I.G. Vlachonikolis, P.E. Kanavaros, and G.S. Delides (1998) Nuclear morphometry of the myocardial cells as a diagnostic tool in cases of sudden death due to coronary thrombosis. Forensic Science International 96:173-180.
17.Liu Xiangbin, Beiji Zou, and Jiaguang Sun (2004) A New Approach to Separating Touching Spots in Particle Images. 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing:133-136.
18.Mery Domingo, and Franco Pedreschi (2005) Segmentation of colour food images using a robust algorithm. JOURNAL OF FOOD ENGINEERING 66:353-360.
19.Ruberto Cecilia Di, Andrew Dempster, Shahid Khan, and Bill Jarra (2002) Analysis of infected blood cell images using morphological operator. Image and Vision Computing 20:133-146.
20.Sammouda Mohamed, Rachid Sammouda, Noboru Niki, Naohito Yamaguchi, and Noriyuki Moriyama (2002) Cancerous nuclei detection on digitized pathological lung color images. Journal of Biomedical Informatics 35:92-98.
21.Tanaka Toshiyuki, Tomoo Joke, and Teruaki Oka (2001) CELL NUCLEUS SEGMENTATION OF SKIN TUMOR USING IMAGE PROCESSING. 23rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society:2716-2719.
22.Wang Sheng-Lan, Hon-Man Chan, Sheau-Fang Yang, Ming-Tsang Wu, and Chee-Yin Chai (2005) Computerized morphometric Study of Thyroid Follicular Carcinoma in Correlation with Known Prognostic Factors. Kaohsiung J Med Sci Vol.21:65-69.
23.Zalewski Kerstin, and Rainer Buchholz (1996) Morphological analysis of yeast cells using an automated image processing system. Journal of Biotechnology 48:43-49.