微晶片相關科技因為能同時研究上萬個基因而廣泛地被各種相關領域的研究人員採用。然而，目前並沒有一個結構化的計算流程存在，從微晶片影像取得開始，可以有效率地探索有研究價值的基因。因此，本篇論文提出了一個計算模組，從微晶片影像計算出基因表現量、正規化校正螢光色差、到找出最有表現的基因以及最能分辨不同特徵病人的基因。
我們比較由我們提出的自動計算基因表現量方法的結果和商用微晶片分析軟體計算的結果，有90.9%的相關係數大於0.5。無法有效提高相關係數的原因在於部分微晶片影像品質不佳。再比較由我們提出的正規化校正色差方法與LOWESS校正方法，也有90.9%的相關係數大於0.9。實驗結果顯示我們的計算模組實用且有效率，可提供微晶片相關計算暨定量檢驗研究的的參考，並對後續臨床診斷與基因分析有助益。

Novel biological technology based on microarray experiments has been extensively applied by various fields of researches due to the utility of microarray capable of investigating tens thousands of genes simultaneously.
However, gene analysis lacks an organized process to effectively discover crucial genes from image acquisition.
Therefore, one computation model was developed to obtain the most differentially expressed genes and the most discriminative genes. We started gene discovery from computing gene expression levels of 44 microarray images by the Otsu thresholding method. Next, the ratios of Cy3 and Cy5 fluorescence intensities of tumor and normal samples respectively of each spot were normalized based on piecewise linear regression method. Finally, thresholding strategies and feature selection methods were utilized to acquire significant genes, accompanied by clustering algorithms to verify the suitability of the selected genes.
Ninety percent of correlation coefficients between our computing data sets of gene expression levels and the ones generated by commercial software (ArrayPro Analyzer) were larger than 0.5. Ninety percent of correlation coefficients between the data sets of our normalized log-ratios and those normalized by LOWESS regression method had values larger than 0.9. These results suggest that our computational model is practical and efficient.
Results in this thesis provide informative materials of computational and quantitative examination for microarray-related research and facilitate clinical diagnosis and gene analysis.

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