Based Adaptive Wavelet Hidden Markov Tree for Microarray Image Enhancement
Cui Li, Ying Li · 2008
The accuracy of the gene expression depends on microarray image processing technology. However, eliminating the noise from different sources inherented in the DNA microarray still a challenging problem, which mainly contribute to the diversity and complexity of the noise of microarray image. Traditionally, statistical methods are used to estimate the noises of the microarray images. In this paper, we construct the adaptive tensor wavelets for microarray image denoising in terms of an explicit parameterizations of the univariate orthogonal scaling functions. The constructed adaptive wavelet keep the edge information as possible as. Combining our constructed adaptive wavelet and hidden Markov tree model, we present a novel image denoising method, which shows the significant improvement for microarray image denoising through the concrete numerical experiments.