title: Segmentation of cDNA microarray images by kernel density estimation.
abstract: 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.
authors.affiliation: Institute of Statistics, National Chiao Tung University, 1101 Ta Hsueh Road, Hsinchu 30010, Taiwan, ROC.
authors.forename: Tai-Been
authors.initials: TB
authors.lastname: Chen
authors.forename: Henry Horng-Shing
authors.initials: HH
authors.lastname: Lu
authors.forename: Yun-Shien
authors.initials: YS
authors.lastname: Lee
authors.forename: Hsiu-Jen
authors.initials: HJ
authors.lastname: Lan
mesh_terms: D018076:DNA, Complementary / Q000235:genetics*; D008962:Models, Theoretical; D020411:Oligonucleotide Array Sequence Analysis
chemical_list: D018076:DNA, Complementary