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

Full Text (PDF, 1246KB), PP.32-39

Views: 0 Downloads: 0


A.Sharmila Agnal 1,* K.Mala 2

1. Department of Computer Science and Engineering, National Engineering College, Kovilpatti, India.

2. Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi, India.

* Corresponding author.


Received: 10 Aug. 2012 / Revised: 19 Sep. 2012 / Accepted: 30 Oct. 2012 / Published: 8 Jan. 2013

Index Terms

CDNA Microarray Images, Denoising Microarray Images, Bivariate LMMSE estimation, Biva-riate MAP estimation, Dual Tree Complex Wavelet Transform


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 ex-pression 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.

Cite This Paper

A.Sharmila Agnal,K.Mala,"Noise Removal From Microarray Images Using Maximum a Posteriori Based Bivariate Estimator", IJIGSP, vol.5, no.1, pp.32-39, 2013. DOI: 10.5815/ijigsp.2013.01.05


[1]Y. Balagurunathan, E. R. Dougherty, Y. Chen, M. L.Bittner, and J. M.Trent, “Simulation of cDNA mi-croarrays via a parameterized random signal model,” J. Biomed. Opt., 2002, vol. 7, no. 3, pp. 507–523.

[2]C. Boncelet, “Image noise models,” in Handbook of Image and Video Processing, A. C. Bovik, Ed. New York: Academic, 2000.

[3]D. Bozinov and J. Rahnenfuhrer, “Unsupervised technique for robust target separation and analysis of DNA microarray spots through adaptive pixel clustering,” Bioinformatics, 2002, vol. 18, pp. 747–756.

[4]T. Cai and B. Silverman, “Incorporating information on neighboring coefficients into wavelet estima-tion,” Sank- hya: Indian J. Statist., 2001 vol. 63, pp. 127–148.

[5]S. W. Davies and D. A. Seale, “DNA microarray stochastic model,” IEEE Trans. Nanobioscience, Sep. 2005, vol. 4, no. 3, pp. 248–254.

[6]D. L. Donoho and I. M. Johnstone, “Adapting to unknown smoothness via wavelet shrinkage,” J. Amer. Statist. Assoc., 1995,vol. 90, no. 432, pp.1200–1224.

[7]J. M. Geoffrey, K. Do, and C. Ambroise, “Analyzing Microarray Gene Expression Data”,Hoboken, NJ: Wiley, 2004.

[8]N. G. Kingsbury, “Complex wavelets for shift invar-iance analysis and filtering of signals,” Appl. Com-put. Harmon. Anal., 2001, vol. 10, no. 3, pp. 234–253.

[9]R. Lukac, K. N. Plataniotis, B. Smolka, and A. N. Venetsanopoulos, “A multichannel order-statistic technique for cDNA microarray image processing,” IEEE Trans. Nanobioscience, Dec.2004,vol. 3, no. 4, pp. 272–285.

[10]S. Mallat, A Wavelet Tour of Signal Processing, 2nd ed. San Diego, CA: Academic, 1999.

[11]L.Sendur and I. W. Selesnick, “Bivariate shrinkage functions for wavelet-based denoising exploiting in-terscale dependency,” IEEE Trans. Signal Process., Nov. 2002, vol. 50, no. 11, pp. 2744–2756.

[12]L. Sendur and I. W. Selesnick, “Bivariate shrinkage with local variance estimation,” IEEE Signal Process. Lett., Dec. 2002, vol. 9, no. 12, pp. 438–441.

[13]I. W. Selesnick, “Hilbert transform pairs of wavelet bases,” IEEE Signal Process. Lett., Jun. 2001, vol. 8, no. 6, pp. 170–173.

[14]I. W. Selesnick, R. G. Baraniuk, and N. G. Kingsbury, “The dual-tree complex wavelet transform,” IEEE Signal Processing Mag., June 2005, vol. 22, no. 6, pp. 123–151.

[15]E. P. Simoncelli and E. Adelson, “Noise removal via Bayesian wavelet coring,” in Proc. IEEE Int. Conf. Image Processing, Lusanne, Switzerland, 1996, vol. 1, pp. 279–382.

[16]Tamanna Howlader and Yogendra P.Chaubey,”Noise Reduction of cDNA Microarray Images using Complex Wavelets”,IEEE transactions on Image Processing, August 2010, Vol. 19, No. 8,pp.1953 -1967.

[17]F. E. Turkheimer, D. C. Duke, L. B. Moran, and M. B. Graeber, “Wavelet analysis of gene expression (WAGE),” in Proc. IEEE Int. Symp. Biomedical Im-aging: Nano to Macro, 2004, vol. 2, pp. 1183–1186.

[18]X. H. Wang, R. S. H. Istepanian, and Y. H. Song, “Microarray imageenhancement by denoising using stationary wavelet transform,” IEEE Trans. Nanobi-osci., Dec. 2003, vol. 2, no. 4, pp. 184–189.

[19]Y. Yang, M. Buckley, S. Dudoit, and T. Speed, “Comparison of methods for image analysis on cDNA microarray data,” J. Comput. Graph. Statist., 2002, vol. 11, pp. 108–136.

[20]Zhang,” Advanced Analysis of Gene Expression Microarray Data”, 1st ed. Singapore: World Scien-tific, 2006.

[21]X. Y. Zhang, F. Chen, Y. Zhang, S. C. Agner, M. Akay, Z. Lu, M. M. Y. Waye, and S. K. Tsui, “Signal processing techniques in genomic engineering,” Proc. IEEE,Dec. 2002, vol. 90, no. 12, pp. 1822–33.

[22]W. Zhang, I.Shmulevich, and J.Astola, “Microarray Quality Control”, 1st ed. Hoboken, NJ: Wiley, 2004.