A Data-Adaptive Approach to cDNA Microarray Image Enhancement

Abstract

Abstract.A data-adaptive approach for cDNA microarray image enhancement is presented. Through the weighting coefficients adaptively determined from local microarray image statistics, the proposed technique tunes the overall filter's detail-preserving and noise-attenuating characteristics and uses both the spatial and spectral correlation of the cDNA image during processing. Noise removal is performed by tuning a membership function which utilizes the aggregated absolute differences between the cDNA microarray inputs localized within a processing window sliding over the image.

A Fully Automatic Approach for Enhancement of Microarray Images

“…A two phase scheme for removing impulse noise from microarray images by preserving the feature of interest is discussed by Ram murugesan et.al [17].Arunakumari Kakumani et.al [18] have proposed a method to denoise microarray images using independent component analysis. Enhancement approach which uses principles of fuzzy logic in conjunction with data adaptive filter to enhance noisy microarray images is presented by Rastislav Lukac et.al [19]. Wang li-qiang et.al [20] presents a novel method to reduce impulse noise by employing the switching scheme which uses differences between the standard deviation of the pixels within the filter window and the current pixel of concern.…”

Standard and Specific Compression Techniques for DNA Microarray Images

“…Many more authors have covered microarray image denoising for genetic information extraction, but only some of the most recent and relevant are referenced here. In 2005, Lukac proposed a method [8] based on fuzzy logic and local statistics for noise removal. Also in 2005, Smolka et al discussed the peer group concept [9] as a means to remove impulsive noise.…”