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Segmentation of cDNA microarray images by kernel density estimation
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DOI：10.1016/j.jbi.2008.02.007.png)
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The segmentation of cDNA microarray spots is essential in analyzing the intensities of microarray images for biological and medical investigation. In this work, nonparametric methods using kernel density estimation are applied to segment two-channel cDNA microarray images. This approach groups pixels into both a foreground and a background. The segmentation performance of this model is tested and evaluated with reference to 16 microarray data. In particular, spike genes with various contents are spotted in a microarray to examine and evaluate the accuracy of the segmentation results. Duplicated design is implemented to evaluate the accuracy of the model. The results of this study demonstrate that this method can cluster pixels and estimate statistics regarding spots with high accuracy. (c) 2008 Elsevier Inc. All rights reserved.
Keyword:
Microarray
Segmentation
Kernel density estimation
Concordance correlation coefficient
Gaussian mixture model
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4.5
论文数:
3.5K
被引数:
1.9W
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