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Automatic Spot Adressing in cDNA Microarray Images
Keywords:
bioinformatics, cDNA microarrays, image analysis, automatic addressingAbstract
Complementary DNA (cDNA) microarrays are a powerful high throughput technology developed in the last decade allowing researchers to analyze the behaviour and interaction of thousands of genes simultaneously. The large amount of information provided by microarray images requires automatic techniques to develop accurate and efficient processing. Each spot in the microarray contains the hybridization level of a single gene. One of the most important features of these images are the regularity and pseudo-periodicity implicit in the spot arrangement. In this paper, an automatic approach based on texture analysis characterization techniques is proposed to localize spots in microarray images. The method estimates the displacement vectors which characterize the texture (i.e. the spot arrangement). This is achieved by means of applying the generalized Hough transform on the 2D autocorrelation function previously segmented via morphological operations. The obtained displacement vectors are used to generate a grid template which overlaps the original image. The Root-Mean-Square-Error (RMSE) between the estimated locations and the ones computed via a semiautomatic tool is calculated to evaluate the accuracy of the process. The method yields promising results.
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References
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[2] Angulo J. and Serra J. Automatic analysis of DNA microarray images using mathematical morphology. Bioinformatics, 19(5):553–562, 2003.
[3] Attwood T. K. and Parry-Smith D. J. Introduction to bioinformatics. Addison Wesley Longman Limited, Harlow, England, 1999.
[4] Bajcsy P. Gridline: Automatic grid alignment in DNA microarray scans. IEEE Trans. on Image Processing, 13(1):15–25, 2004.
[5] Bajcsy P. An overview of DNA microarray grid alignment and foreground separation approaches. EURASIP J. on Applied Signal Proc., Article ID 80163:1–13, 2006.
[6] Berrar D. P., Dubitzky W., and Granzow M., editors. A practical approach to microarray data analysis. Kluwer Academic Publishers, Dordrecht, 2003.
[7] Blekas K., Galatsanos N. P., et al. Mixture model analysis of DNA microarray images. IEEE Trans. on Med. Imag., 24(7):901–909, 2005.
[8] Carstensen J. M. An active lattice model in a Bayesian framework. Computer Vision and Image Understanding, 63(2):380–387, 1996.
[9] Demirkaya O., Asyali M. H., et al. Segmentation of microarray cDNA spots using MRF-based method. 25th Annual Conf. of the IEEE EMBS, Cancun, Mexico, 2003.
[10] Eisen M. Scanalyze. 1999. http://rana.lbl.gov/EisenSoftware.html.
[11] Gonzalez R. and Woods R. Digital image processing. Prentice Hall, 2nd. edition, 2002.
[12] Gonzalez R. C., Woods R. E., and Eddins S. L. Digital image processing using MATLAB. Prentice Hall, 2004.
[13] Hartelius K. and Carstensen J. M. Bayesian grid matching. IEEE Trans. on PAMI, 25(2):162–173, 2003.
[14] Heyer L. J., Moskowitz D. Z., et al. MAGIC tool: integrated microarray data analysis. Bioinformatics, 21(9):2114–2115, 2005.
[15] Hirata Jr. R., Barrera J., et al. Microarray gridding by mathematical morphology. In Proc. SIBGRAPI, Florianópolis, pages 112–119. IEEE, 2001.
[16] Hirata Jr. R., Barrera J., et al. Segmentation of microarray images by mathematical morphology. Real-Time Imaging, 8:491–505, 2002.
[17] Jin H.-J., Chun B.-K., and Cho H.-G. Extended δ-regular sequence for automated analysis of microarray images. EURASIP J. on Applied Signal Proc., Article ID 13623:1–11, 2006.
[18] Katzer M., Kummert F., and Sagerer G. Methods for automatic microarray image segmentation. IEEE Trans. on Nano-Bioscience, 2(4):202–214, 2003.
[19] Li Q., Fraley C., et al. Donuts, scratches and blanks: robust model-based segmentation of mi- croarray images. Bioinformatics, 21(12):2875–2882, 2005.
[20] Li S. Z. Markov Random Field modeling in computer vision. Springer-Verlag, 1995.
[21] Lin H.-C., Wang L.-L., and Yang S.-N. Extracting periodicity of a regular texture based on autocorrelation functions. Pattern Recognition Letters, 18:433–443, 1997.
[22] Liu Y., Collins R. T., and Tsin Y. A computational model for periodic pattern perception based on frieze and wallpaper groups. IEEE Trans. on PAMI, 26(3):354–371, 2004.
[23] Schena M., Shalon D., et al. Quantitative monitoring of gene expression patterns with a complementary cDNA microarray. Science, 270:467–470, 1995.
[24] Yang Y., Buckley M., et al. Comparison of methods for image analysis on cDNA microarray data. Technical Report #584, Dep. of Statistics, UCB, Nov. 2000. http://www.stat.berkeley.edu/users/terry/zarray/TechReport/584.pdf.
[25] S. W. Zucker and D. Terzopoulos. Finding structure in cooccurrence matrices for texture analysis. Computer Graphics and Image Processing, 12(3):286–308, 1980.
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Published
2008-07-01
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[1]
“Automatic Spot Adressing in cDNA Microarray Images”, JCS&T, vol. 8, no. 02, pp. p. 64–70, Jul. 2008, Accessed: Mar. 12, 2026. [Online]. Available: https://journal.info.unlp.edu.ar/JCST/article/view/743
