A New Method to Grid Noisy cDNA Microarray Images Utilizing Denoising Techniques

DNA Microarray is an innovative tool for gene studies in biomedical research, and its applications can vary from cancer diagnosis to human identification. It is capable of testing and extracting the expression of large number of genes in parallel. The gene expression process is divided into three basic steps: gridding, segmentation, and quantification. Automatic gridding; which is to assign coordinates to every element of the spot array, is considered the most challenging phase of microarrays image processing. For processing of microarray images, a new, automatic, fast and accurate approach is proposed for gridding noisy cDNA microarray images. In the real world, microarray image doesn’t reflect measures of the fluorescence intensities for the dye of interest only, as different kinds of noise and artifacts can be observed. In this paper, a novel gridding method based on projection is developed accompanied by a pre-processing, post-processing, and refinement steps for noisy microarray images. Results revealed that the proposed method is used with high accuracy and minimal processing time and can be applied to various types of noisy microarray images.

[1]  Vincent Y. Jouenne Critical Issues in the Processing of cDNA Microarray Images , 2001 .

[2]  Tinku Acharya,et al.  Image Processing: Principles and Applications , 2005, J. Electronic Imaging.

[3]  Frank Y. Shih,et al.  Precise Gridding of Microarray Images by Detecting and Correcting Rotations in Subarrays , 2005 .

[4]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Giuliano Antoniol,et al.  A Markov random field approach to microarray image gridding , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[6]  B. Koop,et al.  Microarray analyses identify molecular biomarkers of Atlantic salmon macrophage and hematopoietic kidney response to Piscirickettsia salmonis infection. , 2004, Physiological genomics.

[7]  Jesús Angulo,et al.  Automatic analysis of DNA microarray images using mathematical morphology , 2003, Bioinform..

[8]  Junior Barrera,et al.  Microarray gridding by mathematical morphology , 2001, Proceedings XIV Brazilian Symposium on Computer Graphics and Image Processing.

[9]  Tessamma Thomas,et al.  Automatic Gridding of DNA Microarray Images using Optimum Subimage , 2009 .

[10]  J. Todd Book Review: Digital image processing (second edition). By R. C. Gonzalez and P. Wintz, Addison-Wesley, 1987. 503 pp. Price: £29.95. (ISBN 0-201-11026-1) , 1988 .

[11]  Y. Tu,et al.  Quantitative noise analysis for gene expression microarray experiments , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Basim Alhadidi,et al.  cDNA Microarray Genome Image Processing Using Fixed Spot Position , 2006 .

[13]  Yu Luo,et al.  Gridding and compression of microarray images , 2004, Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference, 2004. CSB 2004..

[14]  Ajay N. Jain,et al.  Fully automatic quantification of microarray image data. , 2002, Genome research.

[15]  L. Rueda,et al.  Spot Detection and Image Segmentation in DNA Microarray Data , 2005, Applied bioinformatics.