Segmentation of cDNA microarray images by kernel density estimation

Tai Been Chen, Henry Horng Shing Lu*, Yun Shien Lee, Hsiu Jen Lan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

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.

Original languageEnglish
Pages (from-to)1021-1027
Number of pages7
JournalJournal of Biomedical Informatics
Volume41
Issue number6
DOIs
StatePublished - 1 Dec 2008

Keywords

  • Concordance correlation coefficient
  • Gaussian mixture model
  • Kernel density estimation
  • Microarray
  • Segmentation

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