臺灣博碩士論文加值系統

生物晶片(Biochip, Microarray, DNA chip)是一個可以平行篩檢大量生物資訊的工具，它讓我們實現了基因序列分析、功能解讀、疾病治療等夢想。相對的，產生的生物資訊也非常的多，因此，發展自動且精確的晶片影像分析工具是刻不容緩的。我們提出一個影像分析的方法，來解決基因晶片影像中，晶格系統建立(Gridding)以及尋找實驗點(Spot)輪廓的問題。所提出的方法，以主動式輪廓法(Active Contour Model)為主。首先，我們先將彩色影像轉成灰階，利用邊緣偵測(Edge Detection)來強化實驗點(Spot)的邊，然後利用小波轉換分析行總和(Column-sum)以及列總和(Row-sum)剖面圖(profile)來建立晶格系統，以取得每個實驗點的初始輪廓(Initial Contour)。接著再將控制點往質心方向內縮進行能量函數(Energy Function)的最佳化搜尋，以得到較佳的實驗點輪廓。我們所提出的方法，已應用在Stanford Microarray Database的晶片影像中，且得到很好的結果。我們所提出的方法有以下幾個優點：(1) 對於影像傾斜的問題，我們可以自動的校正，不需要人工的調整。(2) 能偵測不規則的行間距與列間距。(3) 不會受到顏色以及實驗點大小的影響。(4) 允許輸入不同數目的頻帶。此外，我們所提出的方法不但可以處理晶片影像中的多個實驗點，並且找到更精確的實驗點輪廓。

DNA microarray is a new tool used to examine expression levels for thousands of genes simultaneously. However, large amounts of bioinformation will be produced in DNA microarray experiments. It’s extremely urgent to develop automatic and precise tools for the analysis of DNA microarray. Image analysis is the first important step for the automatic processing of microarray experiments. In this paper, a novel approach for accurate spot finding in DNA microarray images using active contour model is proposed. In our approach, a microarray image is first transformed into a gray-level one. Wavelet-based profile analysis is then performed for building orthogonal grid system of the image. Each spot is enclosed within a grid after the wavelet-based profile analysis. The boundary of the grid is specified as initial contour of the spot. By shrinking the initial contour through energy conservation, more precise contour of the spot will be located. The approach is tested on microarray images obtained from Stanford Microarray Database. Comprehensive experiments show highly encouraging results. Major contributions of the proposed approach include the followings: (1) We solve the problem of image rotation without manual adjustment. (2) The detection is robust with irregular spot gaps. (3) Our approach is not affected by variations of color and size of spots. (4) Our approach performs well on different number of input channels. Besides, the proposed approach can not only handle multiple spots in microarray images, but also find more precise contours of spots.

中文摘要
英文摘要
誌謝
目 錄
圖目錄
第一章 導論
1.1 研究動機
1.2 研究目的
1.3 前言
第二章 論文架構
2.1 彩色模型轉換
2.2 旋轉校正
2.3 晶格系統之建立
2.4 背景校正與實驗點的排
2.5 以主動式輪廓模型切割實驗點
第三章 旋轉校正
3.1 行總和與列總和剖面圖
3.2 旋轉校正結果
第四章 建立晶格系統
4.1 剖面圖之小波轉換
4.2 尋找晶格點
4.3 各種情形之結果
第五章 實驗點切割演算法
5.1控制點
5.2能量函數
5.3 搜尋方向
5.4能量的正規化
5.5 主動式輪廓模型執行步驟
第六章 實驗結果
第七章 結論
參考文獻

[1] A. D. Baxevanis and B. F. F. Ouellette, Bioinformatics: a practical guide to the analysis of genes and proteins, John Wiley & Sons Inc, 1998.
[2] S. L. Salzberg, D. B. Searls and S, Kasif (Eds.), Computational Methods in Molecular Biology, Elsevier, 1999.
[3] P. O. Brown and D. Botstein, “Exploring the new world of the genome with DNA microarrays,” Nature Genetics Supplement, Volume 21, Page(s): 33-37, 1999.
[4] I. Wickelgren, “DNA chips,” Physician’s Weekly, Volume XIV, No. 43, 1997.
[5] D. E. B. Jr, M. B. Eisen, and M. S. Boguski, “Gene expression informatics — it’s all in your mine,” Nature Genetics Supplement, Volume 21, Page(s): 51-55, 1999.
[6] M.Eisen, ScanAlyze, [Online] Available :
http://rana.lbl.gov/EisenSoftware.html.
[7] Axon Instruments, Inc. GenePix Pro, [Online] Avaliable :
http://www.axon.con/GN_Genomics.html.
[8] Scanalytics, Inc. Microarray Suite, [Online] Available :
http://www.scanalytics.com/product/hts/microarray.html.
[9] H. J. Noordmans, “Detection and characterization of isolated and overlapping spots,” Computer Vision and Image Understanding, Volume 70, Page(s): 23-35, 1998.
[10] N. Brandle, H. Y. Chen, H. Bischof and H. Lapp, “Robust parametric and semi-parametric spot fitting for spot array images,” International Conference on Intelligent Systems for Molecular Biology, Page(s): 20-23, 2000.
[11] Y. Chen, E. R. Dougherty and M. L. Bittner, “Ratio-based decisions and the quantitative analysis of cDNA microarray images,” Journal of Biomedical Optics, Volume 2, Page(s): 364-374, 1997.
[12] M. A. Newton, C. M. Kendzioreki, C. S. Richmond, F. R. Blattner and K. W. Tsui, “On differential variability of expression ratios: Improving statistical inference about gene expression changes from mocroarray data,” Journal of Computational Biology, Volume 8, Page(s): 37-52, 2001.
[13] C. Kooperberg, T. G. Fazzio, J. J. Delrow and T. Tsukiyama, “Improved background correction for spotted DNA microarrays,” Journal of Computational Biology, Volume 9, Page(s): 55-66, 2002.
[14] Y. X. Zhou, P. Kalocsai, J. Y. Chen and S. Shams, “Information processing issues and solutions associated with microarray technology,” Microarray Biochip Technology, Page(s): 167-200, 2000.
[15] T. Srinark and C. Kambhamettu, “A framework for multiple snakes,” IEEE Conference on Computer Vision and Pattern Recognition, Volume 2, Page(s): 202-209, 2001.
[16] C. H. Chuang and W. N. Lie, “Automatic snake contours for the segmentation of multiple objects,” IEEE International Symposium on Circuits and Systems, Volume 2, Page(s): 389-392, 2001.
[17] R. Hirata Jr, R. F. Hashimoto and D. O. Dantas, “Microarray gridding by mathematical morphology,” International Symposium on Computer Graphics, Page(s): 112-119, 2001.
[18] A. W. C. Liew, H. Yan and M. Yang, “Robust adaptive segmentation of DNA microarray images”, Pattern Recognition, Volume 36, Page(s): 1251-1254, 2003.
[19] R. C. Gonzalez and R. E. Woods, Digital image processing. 2nd ed., Prentice Hall, New Jersey, 2002.
[20] M. Kass, A. Witkin and D. Terzopoulos, “Snakes: active contour models,” International Journal of Computer Vision, Volume 1, Page(s): 321-331, 1987.
[21] S. Mallat and S. Zhong, “Characterization of signals from multiscale edges,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 14, Page(s): 710-732, 1992.
[22] L. Cohen and I. Cohen, “Finite-element methods for active contour models and balloons for 2D and 3D images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 15, Page(s): 1131-1147, 1993.
[23] V. Cassells, R. Kimmel and G. Sapiro, “Geodesic active contours,” International Journal of Computer Vision, Volume1, Page(s): 61-79, 1997.
[24] R. Malladi, J. A. Sethian and B. C. Vemuri, “Evolutionary fronts for topology-independent shape Modeling and recovery,” European Conference on Computer Vision, Volume 1, Page(s): 3-13, 1994.
[25] D. Cohen, “On active contour models and balloons,” in Computer Vision, Graphics, and Image Processing: Image Understanding, Volume 53, Page(s): 211-218, 1991.
[26] M. A. Sid-Ahmed, Image processing, theory, algorithms and architectures, McGRAW-Hill, New York, 1995.
[27] Stanford University. Stanford Microarray Database.
http://genome-www5.stanford.edu/. Stanford University, 2001.
[28] L. Bullinger, K. Döhner, E. Bair, S. Fröhling, R. F. Schlenk, R. Tibshirani, H. Döhner and J. R. Pollack, “Use of Gene-Expression Profiling to Identify Prognostic Subclasses in Adult Acute Myeloid Leukemia,” The New England Journal of Medicine, Volume 350, Page(s): 1605-1616, 2004.
[29] J. Y. Kwon, M. HongM, M. S. Choi, S. Kang, K. Duke, S. Kim, S. Lee and J. Lee, “Ethanol-response genes and their regulation analyzed by a microarray and comparative genomic approach in the nematode Caenorhabditis elegans,” International Conference on Structural Genomics, Volume 83, Page(s): 600-614, 2004.