微晶片技術近年來在分子生物學上廣泛的被採用，因為它能同時監控數萬個基因並從中找出與肝癌、胃癌或乳癌等疾病相關的可能潛在基因。在整個微晶片實驗過程中，基因表現量的計算為最重要的一部分，計算的方法愈正確，之後分析的結果也愈有意義。
本篇論文的目的是希望能提供一套簡單、精確、快速並可重複性的微晶片影像分析流程，特別是著重在基因表現量的計算，首先我們介紹了一個快速的定位方法，藉以改善外面商用軟體需大量人工操作與手動調整的缺點，接著提出了三種不同基因表現量的計算方法，並將其計算出的結果與商用軟體的結果做比較，發現之間的相關係數高達95%，之後利用MA圖及兩種不同的正規化方法來選取最有表現量的基因，以供日後更進一步的分析與研究。

Microarray technology has been recently adopted in molecular biology to screen thousands of genes simultaneously and discover the potential genes which are related to a number of diseases such as breast cancer, gastric cancer, hepatoma, and etc. The computation for gene expression is one of the most important parts in the whole processing. The more accurate gene expression one gets, the more significant results of analysis one reaches. The purpose of this thesis is to provide a flowchart for dealing with microarray image pattern analysis fast, accurately and repeatedly with the emphasis on the computation of gene expression.

First, we provide three different methods, Otsu, GMM and ICM, to do gene expression computation. Then do normalization on MA plot to eliminate systematic variations by using two normalization methods: LOWESS and Moving Average Filter. After normalization, we select up-regulated and down-regulated genes for further analysis.

[Alo99] U. Alon et al., “Broad Patterns of Gene Expression Revealed by Clustering Analysis of Tumor and Normal Colon Tissues Probed by Oligonucletide Arrays,” Proceedings of the National Academy of Sciences of the United States of America, Vol. 96, no. 12, pp. 6475-6750, 1999

[Bes74] J. Besag, “Spatial Interaction and the Statistical Analysis of Lattice Systems,” J. Royal Stat. Soc. Ser.B, Vol. 48, pp. 192-236, 1974
[Bes86] J. Besag, “On the Statistical Analysis of Dirty Pictures,” J. Royal Stat. Soc. Ser.B, Vol. 48, pp. 259-302, 1986

[Bra01] A. Brazma and etc., “Minimum information about Microarray Experiment (MIAME) toward Standards for Microarray Data,” Nature Genetics, Vol. 29, pp. 365-371, 2001

[Cau03] H.C. Causton, J. Quackenbush and A. Brazma, “Microarray Gene Expression Analysis,” Blackwell Publishing, 2003

[Che90] C.C Chen and R.C. Dubes, “Environmental Studies of ICM Segmentation Algorithm,” Journal of Information Science and Engineering, 6, pp. 325-337, 1990
[Chu04] H.Y. Chuang, H.F. Liu, S. Brown, et al., "Identifying Significant Genes from Microarray Data," Proceedings of Fourth IEEE Symposium on Bioinformatics and Bioengineering, pp. 358-365, 2004

[Che02] X. Chen, S.T. Cheung, S. So, et al., "Gene Expression Patterns in Human Liver Cancers," Journal of Molecular Biology of the Cell, 13, pp. 1929-1939, 2002

[Cle79] W.S. Cleveland, "Robust Locally Weighted Regression and Smoothing Scatterplots," Journal of the American Statistical Association, Vol. 74, pp. 829-836, 1979

[Cle88] W.S. Cleveland and S.J. Devlin, "Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting," Journal of the American Statistical Association, Vol. 83, pp. 596-610, 1988

[Dem77] A.P. Dempster, N.M. Laird and D.B. Rubin, “Maximum likelihood from incompletedata via the EM algorithm,” Journal of the Royal Statistical Society, 39, pp. 1-38, 1977

[Dub90] R.C. Dubes, A.K Jain, S.C. Sateesha and C.C. Chen, “MRF Model-Based Algorithms for Image Segmentation,” in Proc. IEEE International Conference on Pattern Recognition, pp. 808-814, 1990

[Dud02] S. Dudoit, Y.H. Yang, M. J. Callow and T. P. Speed, "Statistical Methods for Identifying Differentially Expressed Genes in Replicated cDNA Microarray Experiments," Statistica Sinica, 12, pp. 111-139, 2002

[Fuk72] K. Fukunaga, “Introduction to Statistical Pattern Recongnition,” New York Academic, pp. 260-267, 1972

[Gem84] S. Geman and D. Geman, “Stochastic Relaxation: Gibbs Distributions, and the Bayesian Restoration of Images,” IEEE Trans. Pattern Anal. Machine Intell. Vol. 6, pp. 721-741, 1984

[Gho02] D. Ghosh and A.M. Chinnaiyan, “Mixture modeling of gene expression data from Microarray experiments,” Bioinformatics, 18, pp. 275-286, 2002

[Gon02] R.C. Gonzalez and R.E. Woods, “Digital Image Processing,” Prentice-Hall, Inc., 2002

[Han05] D. Hanselman and B. Littlefield, Mastering Matlab 7, pp. 304, 2005

[Kat03] M. Katzer, F. Kummert and G. Sagerer, “Methods for automatic microarray image segmentation,” IEEE Trans. NanoBioscience, 2, pp. 202-214, 2003

[McL02] G.J. McLachlan, R.W. Bean and D. Peel, “A mixture model-based approach to the clustering of microarray expression data,” Bioinformatics, 18, pp. 413-422, 2002

[ONe03] P. O'Neill, G.D. Magoulas and Liu Xiaohui, “Improved processing of microarray data using image reconstruction techniques,” IEEE Trans. NanoBioscience, 2, pp. 176-183, 2003

[Ots79] N. Otsu, "A Threshold Selection Method from Gray-Level Histograms," Journal of IEEE Transactions on Systems, Man, and Cybernetics, SMC-9, pp. 62-66, 1979

[Ros00] D.T. Ross, U. Scherf, M. B. Eisen, et al., "Systematic variation in gene expression patterns in human cancer cell lines," Journal of Nature Genetics, 24, pp. 227-235, 2000

[Wes74] J. S. Weszka, R. N. Nagel and A. Rosenfeld, “A threshold selection technique,” IEEE Trans. Comput., vol. C-23, pp. 1322-1326, 1974
[Web01]http://splweb.bwh.harvard.edu:8000/pages/papers/elena/seg_paper.html

[Web02]http://www.itl.nist.gov/div898/handbook/pmd/section1/pmd144.htm

[Web03] http://www.adobe.com/Support/TechNotes.html

[Web04] ftp://ftp.adobe.com/pub/adobe/DeveloperSupport/TechNotes/PDFfiles