Segmentation of cDNA Microarray Image Using Fuzzy c-Mean Algorithm and Mathematical Morphology

Abstract:

Article Preview

cDNA microarray technology provides an effectual tool to explore the enormous genome. cDNA microarray consists of thousands of gene sequences which are printed on glass slide and these sequence information can be obtained by forming a microarray image. So image analysis is crucial. However, image segmentation is another key point. How to deal with the gene spots which are always comprised with imperfection such as irregular contours, donut shapes, artifact and spots with low expression is important to the robustness of the segmentation method. The paper proposed a method based on fuzzy c-mean algorithm which can effectively avoid the influence of various types of artifacts through adaptive partitioning.

Info:

Periodical:

Edited by:

Long Chen, Yongkang Zhang, Aixing Feng, Zhenying Xu, Boquan Li and Han Shen

Pages:

159-162

DOI:

10.4028/www.scientific.net/KEM.464.159

Citation:

Z. Y. Li and G. R. Weng, "Segmentation of cDNA Microarray Image Using Fuzzy c-Mean Algorithm and Mathematical Morphology", Key Engineering Materials, Vol. 464, pp. 159-162, 2011

Online since:

January 2011

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Price:

$35.00

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