Abstract-Microarray imaging is considered an important tool for large scale analysis of gene expression. The accuracy of the gene expression depends on the experiment itself and further image processing. It's well known that the noises introduced during the experiment will greatly affect the accuracy of the gene expression. How to eliminate the effect of the noise constitutes a challenging problem in microarray analysis. Traditionally, statistical methods are used to estimate the noises while the microarray images are being processed. In this paper, we present a new approach to deal with the noise inherent in the microarray image processing procedure. That is, to denoise the image noises before further image processing using stationary wavelet transform (SWT). The time invariant characteristic of SWT is particularly useful in image denoising. The testing result on sample microarray images has shown an enhanced image quality. The results also show that it has a superior performance than conventional discrete wavelet transform and widely used adaptive Wiener filter in this procedure. Index Terms-Stationary wavelet transform, microarray images, denoising.
The result on the REF shows that the stationary wavelet transform filter able to maintain the time invariance property and has superior performance for denoising microarray image compare with adaptive wiener filter and discrete wavelet transform.
Microarray image enhancement by denoising using stationary wavelet transform
IEEE Transactions on NanoBioscience
IEEE Transactions on NanoBioscience
Medicine Computer Science