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dc.contributor.authorChen, Tai-Beenen_US
dc.contributor.authorLu, Henry Horng-Shingen_US
dc.contributor.authorLee, Yun-Shienen_US
dc.contributor.authorLan, Hsiu-Jenen_US
dc.date.accessioned2014-12-08T15:10:34Z-
dc.date.available2014-12-08T15:10:34Z-
dc.date.issued2008-12-01en_US
dc.identifier.issn1532-0464en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.jbi.2008.02.007en_US
dc.identifier.urihttp://hdl.handle.net/11536/8084-
dc.description.abstractThe 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.en_US
dc.language.isoen_USen_US
dc.subjectMicroarrayen_US
dc.subjectSegmentationen_US
dc.subjectKernel density estimationen_US
dc.subjectConcordance correlation coefficienten_US
dc.subjectGaussian mixture modelen_US
dc.titleSegmentation of cDNA microarray images by kernel density estimationen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.jbi.2008.02.007en_US
dc.identifier.journalJOURNAL OF BIOMEDICAL INFORMATICSen_US
dc.citation.volume41en_US
dc.citation.issue6en_US
dc.citation.spage1021en_US
dc.citation.epage1027en_US
dc.contributor.department統計學研究所zh_TW
dc.contributor.departmentInstitute of Statisticsen_US
dc.identifier.wosnumberWOS:000261220500016-
dc.citation.woscount20-
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