DocumentCode :
2817922
Title :
Microarray Image Processing Using Expectation Maximization Algorithm and Mathematical Morphology
Author :
Guirong, Weng ; Jian, Su
Author_Institution :
Sch. of Mechanic & Electron. Eng., Soochow Univ., Suzhou, China
Volume :
1
fYear :
2009
fDate :
24-26 April 2009
Firstpage :
577
Lastpage :
579
Abstract :
Image processing is an important aspect of microarray experiments. Spots segmentation, which is to distinguish the spot signals from background pixels, is a critical step in microarray image processing. After analyzing other means of microarray segmentation, a new method based on expectation maximization (EM) algorithm, mathematical morphological filtering and morphological processing is presented. And its corresponding theory and realizable steps are introduced in this paper. Simulations show that the new method for spot image segmentation has better performance than most common ways, such as the ScanAlizeTM method and GenePixTM method. The results of experiments, which are computationally attractive, have excellent performance and can preserve structural information while efficiently suppressing noise in DNA microarray data.
Keywords :
bioinformatics; expectation-maximisation algorithm; filtering theory; image denoising; image segmentation; lab-on-a-chip; mathematical morphology; DNA microarray data; GenePixTM method; ScanAlizeTM method; background pixel; expectation maximization algorithm; image processing; mathematical morphological filtering; noise suppression; spot image segmentation; Filtering algorithms; Genetics; Image processing; Image segmentation; Iterative algorithms; Morphology; Pixel; Probes; Signal processing; Visualization; EM Algorithm; Image Segmentation; cDNA Microarray Image; morphological operator;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Sciences and Optimization, 2009. CSO 2009. International Joint Conference on
Conference_Location :
Sanya, Hainan
Print_ISBN :
978-0-7695-3605-7
Type :
conf
DOI :
10.1109/CSO.2009.91
Filename :
5193762
Link To Document :
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