By Topic

Microarray Image Compression Using a Variation of Singular Value Decomposition

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
$33 $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

3 Author(s)
Thomas J. Peters ; Department of Computer Science and Mathematics, Nipissing University, North Bay, ON P1B 8L7 Canada. e-mail: ; Renata Smolikova-Wachowiak ; Mark P. Wachowiak

Microarray images are becoming increasingly important in bioinformatics, proteomics, and in the development of patient-specific therapies. The compression, processing, and analysis of these images are relatively new topics of research. In this paper, we focus on microarray image compression using singular value decomposition (SVD), a well known information compaction method. Although the SVD algorithm produces significant compression results, modifications may lead to further improvements. In an attempt to increase the compression ratio while maintaining a high peak signal-to-noise ratio, we adopt a subdivision scheme wherein the modified SVD is applied on each subimage. Experimental results indicate that SVD approaches are promising in compression, and may also lead to improved postprocessing operations and analysis techniques.

Published in:

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Date of Conference:

22-26 Aug. 2007