IEEE Trans Image Process - Noise reduction of cDNA microarray images using complex wavelets.

Tópicos

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Resumo

Noise reduction is an essential step of cDNA microarray image analysis for obtaining better-quality gene expression measurements. Wavelet-based denoising methods have shown significant success in traditional image processing. The complex wavelet transform (CWT) is preferred to the classical discrete wavelet transform for denoising of microarray images due to its improved directional selectivity for better representation of the circular edges of spots and near shift-invariance property. Existing CWT-based denoising methods are not efficient for microarray image processing because they fail to take into account the signal as well as noise correlations that exist between red and green channel images. In this paper, two bivariate estimators are developed for the CWT-based denoising of microarray images using the standard maximum a posteriori and linear minimum mean squared error estimation criteria. The proposed denoising methods are capable of taking into account both the interchannel signal and noise correlations. Significance of the proposed denoising methods is assessed by examining the effect of noise reduction on the estimation of the log-intensity ratio. Extensive experimentations are carried out to show that the proposed methods provide better noise reduction of microarray images leading to more accurate estimation of the log-intensity ratios as compared to the other CWT-based denoising methods.

Resumo Limpo

nois reduct essenti step cdna microarray imag analysi obtain betterqu gene express measur waveletbas denois method shown signific success tradit imag process complex wavelet transform cwt prefer classic discret wavelet transform denois microarray imag due improv direct select better represent circular edg spot near shiftinvari properti exist cwtbase denois method effici microarray imag process fail take account signal well nois correl exist red green channel imag paper two bivari estim develop cwtbase denois microarray imag use standard maximum posteriori linear minimum mean squar error estim criteria propos denois method capabl take account interchannel signal nois correl signific propos denois method assess examin effect nois reduct estim logintens ratio extens experiment carri show propos method provid better nois reduct microarray imag lead accur estim logintens ratio compar cwtbase denois method

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