DNA microarray image processing based on minimum error segmentation and histogram analysis

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)22_Publication in policy or professional journalNot applicable

2 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Article number65
Pages (from-to)562-568
Journal / PublicationProceedings of SPIE - The International Society for Optical Engineering
Volume5667
Publication statusPublished - 2005

Conference

TitleProceedings of SPIE-IS and T Electronic Imaging - Color Imaging X: Processing, Hardcopy, and Applications
PlaceUnited States
CitySan Jose, CA
Period17 - 20 January 2005

Abstract

DNA microarray allows the monitoring of expressions for tens of thousands of genes simultaneously. Image analysis is an important aspect for microarray experiments that can affect subsequent analysis such as identification of differentially expressed genes. Image processing for microarray images includes three tasks: spot gridding, segmentation and information extraction. In this study, we address the segmentation and information extraction problems, and propose a new segmentation method and a new background and foreground segmentation correction method for accurate information extraction. The initial segmentation is based on minimum error thresholding under the assumption that the probability density distribution of spot image and background image satisfies Gaussian and the final results is obtained through refining initial segmentation by Bayes decision theory. The advantage of our method is that it does not have any restrictions on the spot shape. We compare our experimental results with those obtained from the widely used software GenePix. © 2005 SPIE and IS&T.

Research Area(s)

  • Background correction, Bayes decision theory, DNA Microarray, Image analysis, Minimum error thresholding