Scheduled System Maintenance:
On Monday, April 27th, IEEE Xplore will undergo scheduled maintenance from 1:00 PM - 3:00 PM ET (17:00 - 19:00 UTC). No interruption in service is anticipated.
By Topic

Segmentation of cDNA Microarray Spots Using K-means Clustering Algorithm and Mathematical Morphology

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Hu Yijun ; Sch. of Mechanic & Electron. Eng., Soochow Univ., Suzhou, China ; Weng Guirong

Complementary DNA microarray technology is a powerful tool in many areas. Usually a two channel microarray red-green (RG) image is obtained. Due to the nature of cDNA microarray technology, a number of impairments affect the cDNA microarray image before the analysis such as identification of differentially expressed genes. Microarray image processing plays a crucial role in the extraction and quantitative analysis of the relative abundance of the DNA product. In this paper, a method combined K-means clustering algorithm and mathematical morphology is presented. Mathematical morphology is a useful tool for extracting image components. K-means clustering algorithm has a good performance in the segmentation of microarray image processing. The result of the experiment shows that the method presented in this paper is accurate, automatic and robust.

Published in:

Information Engineering, 2009. ICIE '09. WASE International Conference on  (Volume:2 )

Date of Conference:

10-11 July 2009