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研究生:姜兆剛
研究生(外文):Chao-Kang Chiang
論文名稱:新的二維凝膠電泳影像點偵測法
論文名稱(外文):A novel spot detection for 2-D gel images
指導教授:陳淑媛陳淑媛引用關係
指導教授(外文):Shu-Yuan Chen
學位類別:碩士
校院名稱:元智大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:48
中文關鍵詞:二維凝膠電泳影像點偵測剖面樹狀結構信任值空洞
外文關鍵詞:Gel imagespot detectionslice treeconfidence evaluationvolcanic spot
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隨著生物科技的發展,將資訊工程的技術應用在生物資訊的處理方面也已經越來越受到注意,從早期的手動操作、人工推導,到現在的自動化程序、利用數位模擬等。都是為了能讓使用者能夠更方便且更有效率的使用大量的生物資訊。其演變趨勢從以人力為主,即使用者以人力來比對資料庫的資料,轉變為以電腦為主,亦即由電腦來做自動探勘及分析的工作,除了提高效率,更提升相當的正確性。
本論文中提出一種新的二維凝膠電泳圖點偵測法之改進,目的是解決在進行二維凝膠電泳影像點偵測時,常出現的空洞問題。所提方法以剖面樹狀架構為主,採用平均分群演算法、連結區塊標記法及型態學中的增長與侵蝕來偵測可能出現的空洞情形,藉由改進在偵測空洞時的錯誤來增加其準確性,進而利用所取得的資訊,進行資料庫中龐大蛋白質資訊的比對。並已建構數個實驗證明所提電泳影像點點偵測法是有效且實用的。
Since proteins are directly involved in the biochemical processes of cells and will have differential expression between control and experimental cells, a better understanding of disease or biological characteristic may be archived by identifying the differential expression of proteins between control and experimental samples. Two-dimensional gel images are the best method to analyze complex protein mixtures. However, it is still challenges in the spot detection for 2-D gel images.
A new spot detection 2-D gel images for resolving volcanic spot problem is proposed in this thesis. The proposed method takes slices of a gel image in the gray level direction and builds them into a slice tree, which in turn is used to detect spot and volcanic spot. More specifically, the volcanic spots are detected using the techniques of k-means algorithm, region labeling and morphological operations. After proper handling of the volcanic spots, the accuracy of spot detection can be improved, which in turn to facilitate the consequent spot matching.
Various experiments have been conducted to prove the feasibility and practicality of the proposed method.
摘要 II
ABSTRACT III
誌謝 IV
目錄 V
圖目錄 VIII
表目錄 XI
第一章 序論 1
第二章 背景說明 2
2.1 二維凝膠電泳圖 2
2.2 等電點分離 5
2.3 分子量分離 6
2.4 染色 6
2.5 點偵測 7
2.6 蛋白質的分類 7
2.7 常見問題 8
第三章 相關研究方法 10
3.1 分水嶺法 10
3.2 改良後的分水嶺法 11
3.3 分水嶺法轉換 12
3.4 分水嶺法轉換的缺點 15
第四章 所提方法 17
4.1 剖面樹狀結構 (SLICE TREE) 17
4.2 信任值 18
4.3 使用剖面樹狀結構的缺點 20
4.4 改良 21
4.4.1. 偵測出點的範圍 23
4.4.2. 偵測空洞部分的像素 23
4.4.3. 像素篩選 28
4.4.4. 去除雜訊 31
4.4.5. 變更像素的灰階值 33
第五章 實驗結果與討論 35
5.1 實驗環境與設備 35
5.2 實驗結果 36
5.2.1 執行結果 36
5.2.2 成效分析 40
5.3 討論 43
5.4 辨識錯誤的空洞部份 43
第六章 結論與未來研究 45
6.1 結論 45
6.2 未來研究 45
參 考 文 獻 47
1.T. Aittokallioa, J. Salmib, T.A. Nymanc, O.S. Nevalainenb, "Geometrical distortions in two-dimensional gels:applicable correction methods," Journal of Chromatography B, vol. 37, no. 815,pp. 25–37, 2005.
2.S. Beucher, “Watersheds of functions and picture segmentation,” Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 7, 1928-1931, 1982.
3.D.P. Kreil1, N.A. Karp and K.S. Lilley, “DNA microarray normalization methods can remove bias from differential protein expression analysis of 2D difference gel electrophoresis results,” Bioinformatics, vol. 20, no. 13, pp. 2026-2034, 2004.
4.J.L. Kuo, D.T Lin, E.C Lin and S.Y Huang, “Image analysis system for protein two dimensional gel elelctrophoresis,” Proc. of 16th IPPR Conference on Computer Vision, Graphics and Image Processing, pp. 139-146, 2003.
5.Y.S. Liou, S.Y. Chen, Y.T. Chao, R.S. Liu, Y.C. Tsai, and J.S. Hsieh, “Intelligent spot detection for 2-DE gel image,” Lecture Notes in Computer Science (LNCS), International Conference on IEEE Pacific-Rim Symposium on Image and Video Technology, vol. 4319, pp. 168-177, 2006.
6.S.B.I. Luppens and J.M. ten Cate, “Effect of biofilm model, mode of growth, and strain on streptococcusmutans protein expression as determined by two-dimensional difference gel electrophoresis”, Journal of Proteome Research, vol. 4, no. 2, pp. 232-237, 2005.
7.K.P Plei?獼er, F. Hoffmann, K. Kriegel, C. Wenk, S. Wegner, A. Sahlstr?卌, H. Oswald, H. Alt and E. Fleck, “New algorithmic approaches to protein spot detection and pattern matching in two-dimensional electrophoresis gel databases,” Electrophoresis, vol. 20, no. 4-5, pp. 755-765, 1999.
8.J. Salmi, T. Aittokallio, T.A. Nyman and O.S. Nevalainen, “Correcting distortions in 2D gels – a survey,” Technical Report of Turku Centre for Computer Science, vol. 12, no. 653, pp 1-49, 2004.
9.P.S. Umesh Adiga, A. Bhomra, M.G. Turri, A. Nicod, S.R. Datta, P. Jeavons, R. Mott and J. Flint, “Automatic analysis of agarose gel images,” Bioinformatics, vol. 17, no. 11, pp. 1084-1090, 2001.
10.L. Vincent and P. Soille “Watersheds in digital spaces: an efficient algorithm based on immersion simulations” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 6, pp. 583-597, 1991.
11.S. Veeser, M.J. Dunn, G.Z. Yang “Multiresolution image registration for two-dimensional gel electrophoresis,” Proteomics, vol. 1, no. 80, pp. 856–870, 2001.
12.Website, http://juang.bst.ntu.edu.tw/Pub/SR%20Photo.htm
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