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研究生:葉俊明
研究生(外文):Jun-Ming Ye
論文名稱:分子狀態機在腺癌細胞圖形辨別上的應用
論文名稱(外文):Differentiation of Glandular Cancer Cells and Normal Cells with Cellular Automata
指導教授:陳重臣陳重臣引用關係
指導教授(外文):Jong-Chen Chen
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:資訊管理系碩士班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:中文
論文頁數:65
中文關鍵詞:分子狀態機基因演算法圖形辨識
外文關鍵詞:Genetic AlgorithmsPattern RecognitionCellular Automata
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目前辨別細胞正常或異常的依據,一般是從細胞本身的外形、細胞核與細胞
質比率大小、細胞核染色顏色及細胞活動量而定。在進行判斷的過程,醫生是以
人工的方式來診斷抹片上的細胞分佈及呈現狀況,這樣的方式,就不能避免人為
的疏失因素及當時醫生身體及心理的狀況不適而產生誤判的情形。
因此,本研究針對腺細胞的正常細胞╱癌細胞外形上的差異,以分子狀態機
(Cellular Automata)可動態產生多變的圖形的特點,試圖產生足以辨識正常細胞╱
癌細胞外形的樣版,並期望這些找出來的piece 樣版能夠辨別多個目標圖形,並
有效地區分兩類。研究中藉由基因演算法使分子狀態機尋找樣版的過程中避免在
局部解中徘徊。並實際使用正常細胞╱癌細胞的二維圖形進行實驗。接著,將此
辨識系統再應用於楓葉╱萍蓬草的圖形辨別,其從細胞中正確辨別出癌細胞的比
率達90%;而從葉子中正確辨別出楓葉的比率高達95%。最後再將此系統用於辨
別楓葉中紅楓與青楓的不同,其正確率也高達90%。
There are various approaches used by doctor determine whether a cell is normal or
not, such as appearance, the ratio of nucleus to cytoplasm, nucleus color, and cell
activity. Misjudgment is inevitable as human is to err.
The objective of this study is to dynamically generate template patterns and shape
them in a continuous manner by using cellular automat. Hopefully, we can find out
some patterns that can be used to assist doctor in differentiating normal and abnormal
cells. Genetic Algorithms was used to accumulate learning results and in the mean
time to avoid being caught in local optimum. In the domain of diffentiating normal
and abnormal gland cells, experimental results show that the accuracy rate is 90%.
The system is further applied to differentiate maples and yellow water lily, and the
result shows that it has 95% accurate rate. We then applied it to differentiate red and
green maples with the results of 90% accurate rate.
中文摘要......................................................................................................................... iii
ABSTRACT .................................................................................................................... iv
表目錄 ........................................................................................................................... vi
圖目錄 .......................................................................................................................... vii
第一章 緒論 .................................................................................................................. 8
1.1 研究背景與動機...................................................................................... 8
1.2 研究目的.................................................................................................. 9
1.3 研究流程................................................................................................ 10
1.4 研究限制.................................................................................................11
1.5 論文架構.................................................................................................11
第二章 文獻探討.......................................................................................................... 12
2.1 癌細胞辨識............................................................................................ 12
2.2 圖形辨識(Pattern Recognition).............................................................. 12
2.3 分子狀態機(Cellular Automata)....................................................... 14
2.4 基因演算法(Genetic Algorithms)..................................................... 15
第三章 研究架構與方法.............................................................................................. 19
3.1 分子狀態機規則設計的基因編碼........................................................ 20
3.2 比對函數設計(適應函數) ..................................................................... 22
3.3 基因操作................................................................................................ 26
3.4 研究流程演算法.................................................................................... 28
第四章 實驗設計與結果.............................................................................................. 30
4.1 癌細胞與正常細胞辨別........................................................................ 30
4.2 楓葉與萍蓬草辨別................................................................................ 34
4.3 紅榨槭與青楓辨別................................................................................ 37
第五章 結論與建議...................................................................................................... 41
5.1 實驗結果探討........................................................................................ 41
5.2 結論......................................................................................................... 41
5.3 建議與未來研究方向............................................................................ 42
參考文獻........................................................................................................................ 43
附錄A 研究中使用之細胞原圖形............................................................................ 45
附錄B 實驗一使用之細胞圖形及取邊後圖形對照表............................................ 46
一、 中文文獻
1. 王照元,2002,大腸直腸癌早期診斷標記暨診斷晶片之研發,高雄醫學大學,
博士論文。
2. 何祚明,2001,高頻超音波影像系統,國立臺灣大學,碩士論文。
3. 陳淑嬌,2002,梯度向量流主動輪廓模型於子宮頸抹片細胞邊界偵測之應用,
國立中興大學,碩士論文。
4. 張簡光哲,2002,應用於全乳房篩檢的徒手式超音波之電腦輔助偵測,國立
中正大學,碩士論文。
5. 蔡雅惠,2001,彩色圖形比對:元件搜尋與瑕疵檢測之應用,私立元智大學,
碩士論文。
二、 英文文獻
1. Bye, S.J., Adams, A., 1993, “Neural network paradigm for visual pattern
recognition”, IEEE Artificial Neural Networks, Third International Conference
on , 25-27 May 1993, pp.11-15
2. Chen, C.Y., Hwang, C.J., “A multi-level backpropagation network for pattern
recognition systems”, IEEE World Congress on Computational Intelligence,
International Conference on , 27 June-2 July 1994, pp.3078 - 3082 Vol.5
3. Earl Gose et al., 1996, Pattern recognizition and Image Analysis, Prentice Hall Inc.,
New Jersey,
4. Fischler M. Mattson et al., “An approach to general pattern recognition”,
Information Theory, IEEE Transactions on, Sep 1962, pp.64 – 73 Vol. 8
5. John Holland, 1975, “Adaptation in Natural and Artificial Systems”, University of
Michigan Press, 1975.
6. J. T. Tou et al., 1974, Pattern Recognition Principles, Reading, MA,
Addison-Wesley Publishing Company.
7. L. A. Zadeh, “Fuzzy Set”, Inform. Contr., vol. 8, p. 338, 1965
8. Brady, M.L. et al, 1989, “Probabilistic cellular automata in pattern recognition”,
Neural Networks, IJCNN., International Joint Conference on , 18-22 June 1989
第44 頁
Pages:177 - 182 vol.1
9. Mertzios et al., 1993, “Coordinate logic filters in image processing applications”,
Nonlinear Digital Signal Processing, IEEE Winter Workshop on, 17-20 Jan., 1993
pp.1.1_5.1 - 1.1_5.6
10. S. Wolfram, 1982, “Cellular Automata as Simple Self-Organizing Systems”,
Caltech preprint CALT-68-938.
11. S. Wolfram, 1983, “Cellular Automata”, Reviews of Modern Physics, Vol.55, No.3,
pp.601-644
12. S. Wolfram, 1984, “Cellular Automata as Models of Complexity”, Nature 311,
pp.419-424
三、網路文獻
13. http://www.37c.com.cn/literature/clinic/diseases/02/0214006.html
14. http://www.37c.com.cn/literature/clinic/diseases/12/1206005.html
15. 長庚紀念醫院曾志仁醫師,“子宮頸癌與子宮頸癌前期的防治",中華民國衛
生署子宮頸癌防治教材
http://www.cgmh.org.tw/intr/intr5/c6700/dr2/work/teaching/patient/子宮頸抹片衛
教.htm
16. http://www.cella.cn/book/14/01.htm
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