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A Heuristic Approach to Automatically Segment Signal from Background in DNA Microarray Images

  • S. S. Manjunath
  • Priya Nandihal
  • Lalitha Rangarajan
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 801)

Abstract

Microarray is an efficacious tool used to detect, analyze and describe local features of the genome in the form of an image. A microarray is glass slide which consists of different nucleic acid inquest added chemically. Microarray provides enough guidance on infection pathology, progression, resistance to treatment, and response to cellular microenvironments and ultimately may lead to improved and innovative approaches for disease diagnosis. Study of microarray includes these steps: initially spot addressing followed by spot separation as background and foreground and finally intensity extraction of separated spots. In the spot separation stage, extended k-means and Fast EM methods are proposed. The proposed method is a heuristic approach where Coefficient-Variation of the input image is used as a metric to switch the image for segmentation to the appropriate existing method as well as to the proposed method to efficiently segment the foreground signals from the background in existence of undetermined or unwanted pixel values, and inadequately articulated spots in DNA microarray images. Experimental results and analysis of proposed methods shows promising results when compared with existing methods in the literature.

Keywords

Microarray experiment Expectation maximization K-means Segmentation 

References

  1. 1.
  2. 2.
    Lobenhofer, E.K., Bushel, P.R., Afshari, C.A., Hamadeh, H.K.: Progress in the application of DNA microarrays. Environ. Health Perspect. 109, 881–891 (2001)CrossRefGoogle Scholar
  3. 3.
    Yang, Y.H., Buckley, M.I., Dudoit, S., Speed, T.P.: Comparison of methods for image analysis on cDNA microarray data. J. Comput. Graph. Stat. (2002)Google Scholar
  4. 4.
    Ahmed, A.A., Vias, M., Iyer, N.G., Caldas, C., Brenton, J.D.: Microarray segmentation methods significantly influence data precision. Nucleic Acids Res.Google Scholar
  5. 5.
    Axon Instruments: GenePix A User’s Guide (1999)Google Scholar
  6. 6.
    Eisen, M.B.: ScanAlyze http://rana.Stanford.EDU/software/ for software and documentation
  7. 7.
    Automatic techniques for gridding cDNA microarray imagesGoogle Scholar
  8. 8.
    Smyth, G.K., Yang, Y.H., Speed, T.: Statistical issues in cDNA microarray data analysis functional genomics: methods and protocols. In: Brownstein, M.J., Khodursky, A.B. (eds.) Methods in Molecular Biology Series Totowa. Humana Press, New York City (2002)Google Scholar
  9. 9.
    Buhler, J., Ideker, T., Haynor, D.: Dapple: improved techniques for finding spots on DNA microarrays. Washington UWTR Department of Computer Science and Engineering, University of Washington (2000)Google Scholar
  10. 10.
    GSI Lumonics: QuantArray analysis software, operator’s manual (1999)Google Scholar
  11. 11.
    Siddiqui, K.I., Hero, A., Siddiqui, M.: Mathematical morphology applied to spot segmentation and quantification of gene microarray images In: Proceedings of Asilomar Conference on Signals and Systems (2002)Google Scholar
  12. 12.
    Buckley, M.J.: Spot User’s Guide. CSIRO Mathematical and Information Sciences, Sydney (2002)Google Scholar
  13. 13.
    Wang, X., Ghosh, S., Guo, S.W.: Quantitative quality control in microarray image processing and data acquisition. Nucleic Acids Res. 29(15), e75 (2001)CrossRefGoogle Scholar
  14. 14.
    ImaGene, ImaGene 6.1 User Manual (2008). http://www.biodiscovery.com/index/papps-webfiles-action
  15. 15.
    Srinark, T., Kambhamettu, C.: A microarray image analysis system based on multiple snakes. J. Biol. Syst. Spec. 12 (2004)Google Scholar
  16. 16.
    Blekas, K., Galatsanos, N.P., Georgiou, I.: An unsupervised artifact correction approach for the analysis of DNA microarray images. In: Proceedings of the IEEE International Conference on Image Processing (ICIP), vol. 2 (2003)Google Scholar
  17. 17.
    Blekas, K., Galatsanos, N., Likas, A., Lagaris, I.E.: Mixture model analysis of DNA microarray images. IEEE Trans. Med. Imaging 24(7) (2005)Google Scholar
  18. 18.
    Ceccarelli, M., Antoniol, G.: A deformable grid-matching approach for microarray images. IEEE Trans. Image Process. (2006)Google Scholar
  19. 19.
    Demirkaya, O., Asyali, M.H., Shoukri, M.M., Abu-Khabar, K.S.: Segmentation of microarray cDNA spots using MRF-based method. In Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 1 (2003)Google Scholar
  20. 20.
    Noba, H., Shirazi, M.N., Kawaguchi, E.: MRF-based texture segmentation using wavelet decomposed images. Pattern Recogn. 35 (2002)Google Scholar
  21. 21.
    Ergüt, E., Yardimci, Y., Mumcuoglu, E., Konu, O.: Analysis of microarray images using FCM and K-means clustering algorithm. In: Proceedings of International Conference on Signal Processing (2003)Google Scholar
  22. 22.
    Wu, S., Yan, H.: Microarray image processing based on clustering and morphological analysis. In: Proceedings of 1st Asia Pacific Bioinformatics Conference, vol. 2 (2003)Google Scholar
  23. 23.
    Bozinov, D., Rahnenführer, J.: Unsupervised technique for robust target separation and analysis of DNA microarray spots through adaptive pixel clustering. Bioinformatics (2002)Google Scholar
  24. 24.
    Rahnenführer, J., Bozinov, D.: Hybrid clustering for microarray image analysis combining intensity and shape features. BMC Bioinf. (2004)Google Scholar
  25. 25.
    Abbaspour, M., Abugharbieh, R., Podder, M., Tripp, B.W., Tebbutt, S.J.: Hybrid spot segmentation in four-channel microarray genotyping image data. In: IEEE International Symposium on Signal Processing and Information Technology (2006)Google Scholar
  26. 26.
    Dempster, A., Laird, N., Rubin. D.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. 39(1) (2006)Google Scholar
  27. 27.
    Lehmussola, A., Ruusuvuori, P., Yli-Harja, O.: Evaluating the performance of microarray segmentation algorithms. Bioinformatics (2006)Google Scholar
  28. 28.
    Athanasiadis, E.I., Cavouras, D.A., Spyridonos, P.P., Glotsos, D.T., Kalatzis, I.K., Nikiforidis, G.C.: Complementary DNA microarray image processing based on the Fuzzy Gaussian mixture model. IEEE Trans. Inf Technol. Biomed. 13(4), 419–425 (2009)CrossRefGoogle Scholar
  29. 29.
    Demirkaya, O., Asyali, M.H., Shoukri, M.M.: Segmentation of cDNA microarray spots using markov random field modeling. Bioinformatics 21(13), 2994–3000 (2005)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Dayananda Sagar Academy of Technology and ManagementBangaloreIndia
  2. 2.University of MysoreMysoreIndia

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