Quantitative Improvements in cDNA Microarray Spot Segmentation
When developing a cDNA microarray experiment, the segmentation of individual spots is a crucial stage. Spot intensity measurements and gene expression ratios directly depend on the effectiveness and accuracy of the segmentation results. However, since the ground truth is unknown in microarray experiments, quantification of the accuracy of the segmentation process is a very difficult task. In this paper an improved unsupervised technique based on the combination of clustering algorithms and Markov Random Fields (MRF) is proposed to separate the foreground and background intensity signals used in the spot ratio computation. The segmentation algorithm uses one of two alternative methods to provide for initialization, namely K-means and Gaussian Mixture Models (GMM) clustering. This initial segmentation is then processed via MRF. Accuracy is measured by means of a set of microarray images containing spike spots where the target ratios are known a priori, thus making it possible to quantify the expression ratio errors. Results show improvements over state-of-the-art procedures.
KeywordsGene expression cDNA microarray segmentation Gaussian Mixture Models K-means clustering Markov Random Field segmentation quantitative segmentation errors
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