標題: Segmentation of cDNA microarray images by kernel density estimation
作者: Chen, Tai-Been
Lu, Henry Horng-Shing
Lee, Yun-Shien
Lan, Hsiu-Jen
統計學研究所
Institute of Statistics
關鍵字: Microarray;Segmentation;Kernel density estimation;Concordance correlation coefficient;Gaussian mixture model
公開日期: 1-Dec-2008
摘要: 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.
URI: http://dx.doi.org/10.1016/j.jbi.2008.02.007
http://hdl.handle.net/11536/8084
ISSN: 1532-0464
DOI: 10.1016/j.jbi.2008.02.007
期刊: JOURNAL OF BIOMEDICAL INFORMATICS
Volume: 41
Issue: 6
起始頁: 1021
結束頁: 1027
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