本文主要是對 cDNA 微陣列影像，提供一個基因表現比值的穩健估計方法，稱為META。目前文獻在微陣列影像處理上，在經過格線劃分後，多數著重在基因點影像位元素之型態分割, 藉以得到前景光與背景光的強度值估計，再推算基因表現值。但在型態分割的過程中，往往因為雜訊干擾造成分割不完全，以致所估算的前景光訊息比背景光微弱，所得到的基因表現值亦因而不可靠。本論文嘗試不經過既定的前、背景光型態分割過程，而是以各個位元素上 (pixel-by-pixel) 的基因表現值，藉由同心圓區域分組方式，依各區變異給予相對應之權數，再估算出整個點之基因表現值。透過七種點影像模式設定下，分別進行2000次模擬。 META在多數情況下，都能有令人滿意的表現比值估計。進一步經過實例分析，不論有無雜訊干擾，META 所得到的基因表現值，比已知的方法(如 k-means 分割法、GTMM，或是紅綠光迴歸係數斜率值法) 所得到的 MSE 都還要小。本文另提出點品質指標 (spot quality metric)，以進一步確認較佳的基因表現估計值進入後端分析，提供 cDNA 微陣列更可靠的結果。

In microarray processing, the appearance of artifacts, donuts, and irregularly shaped spots is a problem. In current microarray analysis, most approaches stress the segmentation of pixel intensities rather than emphasizing ratio estimators. To avoid segmenting spot target areas and to minimize sensitivity to aberrant pixels, we propose a robust ratio estimator of gene expression via inverse-variance weighting. Moreover, a metric is proposed to evaluate the spot quality. Image simulations present that most of setting artifact spots can be estimated satisfactorily by META.
Results from numerical examples explored reveal that the proposed algorithm is superior to existing approaches with respect to mean square error. The acceptance quality measure recommended confirms the validity of the proposed ratio estimator.

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