The Community for Technology Leaders
RSS Icon
Issue No.01 - Jan.-Feb. (2013 vol.10)
pp: 181-192
Gui-Fang Shao , Dept. of Autom., Xiamen Univ., Xiamen, China
Fan Yang , Dept. of Autom., Xiamen Univ., Xiamen, China
Qian Zhang , Dept. of Autom., Xiamen Univ., Xiamen, China
Qi-Feng Zhou , Dept. of Autom., Xiamen Univ., Xiamen, China
Lin-Kai Luo , Dept. of Autom., Xiamen Univ., Xiamen, China
Gridding is the first and most important step to separate the spots into distinct areas in microarray image analysis. Human intervention is necessary for most gridding methods, even if some so-called fully automatic approaches also need preset parameters. The applicability of these methods is limited in certain domains and will cause variations in the gene expression results. In addition, improper gridding, which is influenced by both the misalignment and high noise level, will affect the high throughput analysis. In this paper, we have presented a fully automatic gridding technique to break through the limitation of traditional mathematical morphology gridding methods. First, a preprocessing algorithm was applied for noise reduction. Subsequently, the optimal threshold was gained by using the improved Otsu method to actually locate each spot. In order to diminish the error, the original gridding result was optimized according to the heuristic techniques by estimating the distribution of the spots. Intensive experiments on six different data sets indicate that our method is superior to the traditional morphology one and is robust in the presence of noise. More importantly, the algorithm involved in our method is simple. Furthermore, human intervention and parameters presetting are unnecessary when the algorithm is applied in different types of microarray images.
Noise, Morphology, Fluorescence, Image segmentation, Information filters, Feature extraction,mathematical morphology, cDNA microarray, gridding, Otsu method
Gui-Fang Shao, Fan Yang, Qian Zhang, Qi-Feng Zhou, Lin-Kai Luo, "Using the Maximum Between-Class Variance for Automatic Gridding of cDNA Microarray Images", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.10, no. 1, pp. 181-192, Jan.-Feb. 2013, doi:10.1109/TCBB.2012.130
[1] P. Bajcsy, “An Overview of DNA Microarray Image Requirements for Automated Processing,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition (CVPR '05), p. 147, 2005.
[2] Q. Li et al., “Donuts, Scratches and Blanks: Robust Model-Based Segmentation of Microarray Images,” Bioinformatics, vol. 22, pp. 2875-2882, 2005.
[3] B. Alhadidi, H.N. Fakhouri, and O.S. Al Mousa, “cDNA Microarray Genome Image Processing Using Fixed Spot Position,” Am. J. Applied Sciences, vol. 3, pp. 1730-1734, 2006.
[4] O. Demirkaya, M.H. Asyali, and M.M. Shoukri, “Segmentation of cDNA Microarray Spots Using Markov Random Field Modeling,” Bioinformatics, vol. 21, no. 13, pp. 2994-3000, 2005.
[5] V. Uslan and Đ. Ömür Bucak, “MICROARRAY IMAGE SEGMENTATION USING CLUSTERING2288;METHODS,” Math. and Computational Applications, vol. 15, no. 2, pp. 240-247, 2010.
[6] Y.H. Yang, M.J. Buckley, S. Dudoit, and T.P. Speed, “Comparison of Methods for Image Analysis on cDNA Microarray Data,” J. Computing Graphical Statistics, vol. 11, pp. 108-136, 2002.
[7] P. Bajcsy, “An Overview of DNA Microarray Grid Alignment and Foreground Separation Approaches,” EURASIP J. Applied Signal Processing, vol. 2006, pp. 1-13, 2006.
[8] M.B. Eisen, ScanAlyze EB/OL], http://rana.stanford.edusoftware, 2002.
[9] Axon Instruments, Inc., GenePix 4000A User's Guide, CA: Union City, 1999.
[10] GSI Luminomics. Quant Array Analysis Software, Operator's Manual, 1999.
[11] J. Deepa and T. Thomas, “Automatic Gridding of DNA Microarray Images Using Optimum Subimage,” Int'l J. Recent Trends in Eng., vol. 1, no. 4, pp. 37-40, May 2009.
[12] Biodiscovery, Inc,: ImaGene, imagene.asp. 2007.
[13] L. Rueda and B. Vidyadharan, “A Hill-Climbing Approach for Automatic Gridding of cDNA Microarray Images,” IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 3, no. 1, pp. 72-83, Jan.-Mar. 2006.
[14] E. Zacharia and D. Maroulis, “An Original Genetic Approach to the Fully Automatic Gridding of Microarray Images,” IEEE Trans. Medical Imaging, vol. 27, no. 6, pp. 805-813, June 2008.
[15] D. Bariamis, D.K. Iakovidis, and D. Maroulis, “M3G: Maximum Margin Microarray Gridding,” BMC Bioinformatics, vol. 11, article 49, pp. 1-11, 2010.
[16] L. Rueda and I. Rezaeian, “A Fully Automatic Gridding Method for cDNA Microarray Images,” BMC Bioinformatics, vol. 12, article 113, pp. 1-17, 2011.
[17] J. Angulo and J. Serra, “Automatic Analysis of DNA Microarray Images Using Mathematical Morphology,” Bioinformatics, vol. 19, no. 5, pp. 553-562, 2003.
[18] N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Trans. Systems, Man, and Cybernetics, vol. SMC-9, no. 1, pp. 62-66, Jan. 1979.
[19] G.F. Shao et al, “Noise Estimation and Reduction in Microarray Images,” Proc. WRI World Congress Computer Science Information Eng. (CSIE '09), vol. 1, pp. 564-568, 2009.
124 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool