cDNA Microarray Basics
cDNA Microarray Imaging
cDNA Image Noise
cDNA Characteristics
cDNA Image Denoising
cDNA Data Normalization
cDNA Spot Localization
cDNA Spot Segmentation


cDNA Image Denoising and Enhancement

To reduce, if not eliminate, noise and impairments present in the acquired microarray image, thus reducing processing errors from propagating further down the processing pipeline to the essential gene expression analysis, image processing operations, such as denoising and enhancement, data normalization, and spot localization and segmentation, are routinely utilized.

Since microarray images are nonlinear in nature, nonlinear methods can potentially preserve important structural elements such as spot edges, and at the same time eliminate microarray image impairments. Given the fact that cDNA microarray images are represented as a two-dimensional array of two-component cDNA vectors, the appropriate cDNA microarray processing solution should integrate well-known concepts from the areas of nonlinear filtering, multidimensional scaling, and robust estimation theory.

Acquired cDNA image


Denoised/enhanced image


The most commonly used method to decrease the level of random noise present in the signal is smoothing. Therefore, a low-pass filtering operation is required in order to replace the noisy cDNA vectors with suitable cDNA vector representatives for the local microarray image area determined by the supporting window. One of the most natural approaches of identifying the atypical, noisy samples in the data population is the sample ordering which can be used to determine the positions of the different input vectors without any prior information regarding the signal distributions. Thus, vector order-statistics filters are considered to be robust estimators and have been successfully used in cDNA microarray image processing. Alternatively, fuzzy vector filters are highly appropriate for microarray image denoising and enhancement because they are capable to overcome non-stationarity in edges and can distinguish between noise and edge pixels.


bulletR. Lukac and K.N. Plataniotis, "cDNA Microarray Image Segmentation Using Root Signals,"  International Journal of Imaging Systems and Technology, vol. 16, no. 2, pp. 51-64, April 2006.
bulletR. Lukac, K.N. Plataniotis, B. Smolka, and A.N. Venetsanopoulos, "cDNA Microarray Image Processing Using Fuzzy Vector Filtering Framework," Fuzzy Sets and Systems, Special Issue on Fuzzy Sets and Systems in Bioinformatics, vol. 152, no. 1, pp.17-35, May 2005.
bulletR. Lukac, B. Smolka, K. Martin, K.N. Plataniotis, and A.N. Venetsanopoulos, "Vector Filtering for Color Imaging," IEEE Signal Processing Magazine, Special Issue on Color Image Processing, vol. 22, no. 1, pp. 74-86, January 2005.
bulletR. Lukac, K.N. Plataniotis, B. Smolka, and A.N. Venetsanopoulos, "A Multichannel Order-Statistic Technique for cDNA Microarray Image Processing," IEEE Transactions on NanoBioscience, vol. 3, no. 4, pp. 272-285, December 2004.

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Last update: 10/15/06

2006 Rastislav Lukac