Microarray image enhancement by denoising using stationary wavelet transform

Wang, Xinheng; Istepanian, R.S.H.; Song, Yun Heub

doi:10.1109/tnb.2003.816225

Cited by 172 publications

(99 citation statements)

References 25 publications

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“…Reduction of noise from microarray images can be performed in the pixel-domain [9], or in a transform domain [18].…”

Section: Related Workmentioning

confidence: 99%

Noise Removal From Microarray Images Using Maximum a Posteriori Based Bivariate Estimator

Agnal¹,

Mala²

2013

IJIGSP

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Abstract-Microarray Image contains information about thousands of genes in an organism and these images are affected by several types of noises. They affect the circular edges of spots and thus degrade the image quality. Hence noise removal is the first step of cDNA microarray image analysis for obtaining gene expression level and identifying the infected cells. The Dual Tree Complex Wavelet Transform (DT-CWT) is preferred for denoising microarray images due to its properties like improved directional selectivity and near shift-invariance. In this paper, bivariate estimators namely Linear Minimum Mean Squared Error (LMMSE) and Maximum A Posteriori (MAP) derived by applying DT-CWT are used for denoising microarray images. Experimental results show that MAP based denoising method outperforms existing denoising techniques for microarray images.

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“…Reduction of noise from microarray images can be performed in the pixel-domain [9], or in a transform domain [18].…”

Section: Related Workmentioning

confidence: 99%

Noise Removal From Microarray Images Using Maximum a Posteriori Based Bivariate Estimator

Agnal¹,

Mala²

2013

IJIGSP

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“…Previous studies have used methods for image enhancement to address the effect of the microarray image noise [32], [17], [16], [18], [19]. Daskalakis et al [1] introduced a complete framework for microarray image analysis, which takes into account the effect of local spot-image noise in microarray images for improving spot segmentation and subsequently gene quantification.…”

Section: Introductionmentioning

confidence: 99%

Microarray Image Denoising Using a Two-Stage Multiresolution Technique

Veljkovic

Robbins²,

Rubino

et al. 2007

2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007)

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DNA microarrays have demonstrated an excellent potential in correlating specific gene expression profiles to specific conditions. However, they are affected by inherent noise. This paper presents a two-stage approach for noise removal that processes the additive and the multiplicative noise component. The proposed approach first decomposes the signal by a multiresolution transform and then accounts for both the multiscale correlation of the subband decompositions and their heavy-tailed statistics. Real microarray images have been processed by the proposed method and its improved performance is shown through quantitative measures and qualitative visual evaluation.

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“…It should be pointed out that the large number of spots (usually in thousands) as well as the spot's shape and position irregularities [18,19] could result in processing errors propagating through succeeding analysis steps [3,20,21]. It has been anticipated that the spot intensity value would be independent of the segmentation algorithm if a background correction method is used [5].…”

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confidence: 99%

A novel neural network approach to cDNA microarray image segmentation

Wang

Zineddin

Liang

et al. 2013

Computer Methods and Programs in Biomedicine

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Microarray technology has become a great source of information for biologists to understand the workings of DNA which is one of the most complex codes in nature. Microarray images typically contain several thousands of small spots, each of which represents a different gene in the experiment. One of the key steps in extracting information from a microarray image is the segmentation whose aim is to identify which pixels within an image represent which gene. This task is greatly complicated by noise within the image and a wide degree of variation in the values of the pixels belonging to a typical spot. In the past there have been many methods proposed for the segmentation of microarray image. In this paper, a new method utilizing a series of artificial neural networks, which are based on multi-layer perceptron (MLP) and Kohonen networks, is proposed. The proposed method is applied to a set of real-world cDNA images. Quantitative comparisons between the proposed method and commercial software GenePix are carried out in terms of the peak signal-to-noise ratio (PSNR). This method is shown to not only deliver results comparable and even superior to existing techniques but also have a faster run time.

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Microarray image enhancement by denoising using stationary wavelet transform

Cited by 172 publications

References 25 publications

Noise Removal From Microarray Images Using Maximum a Posteriori Based Bivariate Estimator

Noise Removal From Microarray Images Using Maximum a Posteriori Based Bivariate Estimator

Microarray Image Denoising Using a Two-Stage Multiresolution Technique

A novel neural network approach to cDNA microarray image segmentation

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