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Noise Removal using Empirical Mode Decomposition and Wavelet Transform in Microarray Images

International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 68 - Number 2
Year of Publication: 2013
D. Rama Krishna
J. Harikiran
P. V. Lakshmi
K. Ramesh

Rama D Krishna, J Harikiran, P V Lakshmi and K Ramesh. Article: Noise Removal using Empirical Mode Decomposition and Wavelet Transform in Microarray Images. International Journal of Computer Applications 68(2):1-7, April 2013. Full text available. BibTeX

	author = {D. Rama Krishna and J. Harikiran and P. V. Lakshmi and K. Ramesh},
	title = {Article: Noise Removal using Empirical Mode Decomposition and Wavelet Transform in Microarray Images},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {68},
	number = {2},
	pages = {1-7},
	month = {April},
	note = {Full text available}


A Deoxyribonucleic Acid (DNA) microarray is a collection of microscopic DNA spots attached to a solid surface, such as glass, plastic or silicon chip forming an array. The analysis of DNA microarray images allows the identification of gene expressions to draw biological conclusions for applications ranging from genetic profiling to diagnosis of cancer. The DNA microarray image analysis includes three tasks: gridding, segmentation and intensity extraction. In this paper, a method for noise removal in microarray image using Bi-dimensional Empirical Mode Decomposition (BEMD) is presented. The BEMD decomposes the image into IMFs and residual components. Then, the selected high frequency IMFs are de-noised with wavelet model and finally the BEMD reconstruction gives the de-noised image. The experimental results show the proposed algorithm can perform significantly better in terms of noise suppression and detail preservation in microarray images.


  • M. Schena, D. Shalon, Ronald W. davis and Patrick O. Brown, " Quantitative Monitoring of gene expression patterns with a complementary DNA microarray", Science, 270,199,pp:467-470.
  • Wei-Bang Chen, Chengcui Zhang and Wen-Lin Liu, "An Automated Gridding and Segmentation method for cDNA Microarray Image Analysis", 19th IEEE Symposium on Computer-Based Medical Systems.
  • Tsung-Han Tsai Chein-Po Yang, Wei-ChiTsai, Pin-Hua Chen, "Error Reduction on Automatic Segmentation in Microarray Image", IEEE 2007.
  • Eleni Zacharia and Dimitirs Maroulis, "Microarray Image Analysis based on an Evolutionary Approach" 2008 IEEE.
  • N. E. Huang, Z. Shen, S. R. Long, "The empirical mode decomposition and the Hilbert Spectrum for non-linear and non-stationary time series analysis". Proc. Roy. Soc, London. A, Vol. 454, pp. 903-995, 1998.
  • Liu Xin-xia, Hau Fu-lian, Wang Jin-gui", Wavelet Extended EMD Noise Reduction Model for Signal Trend Extraction", 2009 IEEE.
  • x. -P. Zhang and M. D. Desai, "Adaptive denoising based on SURE risk," IEEE Signal Process. Lett. , vol. 5, no. 10, pp. 265-267, Oct. 1998.
  • D. L. Donoho and I. M. Johnstone, "Denoising by soft thresholding", IEEE Trans. on Iriform. Theory, Vol. 41, pp. 613-627, 1995.
  • T. D. Bui and G. Y. Chen, "Translation-invariant denoising using multiwavelets", IEEE Transactions on Signal Processing, Vo1. 46, No. l2, pp. 3414-3420, 1998.
  • Sachin Ruikar, Dr. DD Doye, " Image Denoising Using Wavelet Transform", 2010 International Conference on Mechanical and Electrical technology, ICMET 2010.