Microarray image compression using a variation of singular value decomposition.
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 post-processing operations and analysis techniques.
Peters
Thomas J
TJ
Department of Computer Science and Mathematics, Nipissing University, North Bay, ON P1B 8L7, Canada. tjpeters482@community.nipissingu.ca
Smolíková-Wachowiak
Renata
R
Wachowiak
Mark P
MP
D000465:Algorithms; D044962:Data Compression / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D017404:In Situ Hybridization, Fluorescence / Q000379:methods*; D008856:Microscopy, Fluorescence / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*