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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/5959

Title: Cellular neural networks, Navier-Stokes equation and microarray image reconstruction
Authors: Zineddin, B
Wang, Z
Liu, X
Keywords: cDNA microarray reconstruction
Cellular neural networks (CNN)
Isotropic diffusion
Navier-Stokes equations (NSEs)
Partial differential equations (PDEs)
Publication Date: 2011
Publisher: IEEE
Citation: IEEE Transactions on Image Processing, 20(11): 3296 - 3301, Nov 2011
Abstract: Although the last decade has witnessed a great deal of improvements achieved for the microarray technology, many major developments in all the main stages of this technology, including image processing, are still needed. Some hardware implementations of microarray image processing have been proposed in the literature and proved to be promising alternatives to the currently available software systems. However, the main drawback of those proposed approaches is the unsuitable addressing of the quantification of the gene spot in a realistic way without any assumption about the image surface. Our aim in this paper is to present a new image-reconstruction algorithm using the cellular neural network that solves the Navier–Stokes equation. This algorithm offers a robust method for estimating the background signal within the gene-spot region. The MATCNN toolbox for Matlab is used to test the proposed method. Quantitative comparisons are carried out, i.e., in terms of objective criteria, between our approach and some other available methods. It is shown that the proposed algorithm gives highly accurate and realistic measurements in a fully automated manner within a remarkably efficient time.
Description: Copyright @ 2011 IEEE.
URI: http://ieeexplore.ieee.org/servlet/opac?punumber=83
http://bura.brunel.ac.uk/handle/2438/5959
DOI: http://dx.doi.org/10.1109/TIP.2011.2159231
ISSN: 1057-7149
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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