Microarray Image Denoising Using Complex Gaussian Scale Mixtures of Complex Wavelets

Microarray images when contaminated with noise may severely affect the detection and quantification of gene expression. In this paper, we propose to use the complex Gaussian scale mixture (CGSM) model in complex wavelet domain for noise reduction in complementary DNA microarray images. Based on the...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2014-07, Vol.18 (4), p.1423-1430
Main Authors: Srinivasan, Lakshmi, Rakvongthai, Yothin, Oraintara, Soontorn
Format: Article
Language:English
Subjects:
Algorithms
Computational Biology - methods
Image Processing, Computer-Assisted - methods
Joints
Noise reduction
Normal Distribution
Oligonucleotide Array Sequence Analysis - methods
PSNR
Vectors
Wavelet Analysis
Wavelet transforms
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Summary:Microarray images when contaminated with noise may severely affect the detection and quantification of gene expression. In this paper, we propose to use the complex Gaussian scale mixture (CGSM) model in complex wavelet domain for noise reduction in complementary DNA microarray images. Based on the joint information in the red and green channel microarray images, we model the complex wavelet coefficients of the channel images jointly using the CGSM, and subsequently perform image denoising using Bayes least square estimator in complex wavelet domain. The experimental results show that using the CGSM of complex wavelet coefficients provides better noise reduction of microarray images compared to other complex wavelet-based models.
ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2014.2318279