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

Zhi Yao Li; Gui Rong Weng

doi:10.4028/www.scientific.net/KEM.464.159

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HomeKey Engineering MaterialsKey Engineering Materials Vol. 464Segmentation of cDNA Microarray Image Using Fuzzy...

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

Abstract:

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.

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Periodical:

Key Engineering Materials (Volume 464)

Pages:

159-162

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.464.159

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Online since:

January 2011

Authors:

Zhi Yao Li, Gui Rong Weng

Keywords:

cDNA Microarray, Fuzzy C-Means (FCM), Image De-Noising, Image Segmentation

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