Advertisement

A Pattern Classification Approach to DNA Microarray Image Segmentation

  • Luis Rueda
  • Juan Carlos Rojas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5780)

Abstract

A new method for DNA microarray image segmentation based on pattern recognition techniques is introduced. The method performs an unsupervised classification of pixels using a clustering algorithm, and a subsequent supervised classification of the resulting regions. Additional fine tuning includes detecting region edges and merging, and morphological operators to eliminate noise from the spots. The results obtained on various microarray images show that the proposed technique is quite promising for segmentation of DNA microarray images, obtaining a very high accuracy on background and noise separation.

Keywords

DNA microarray images segmentation clustering classification 

References

  1. 1.
    Yang, Y., Buckley, M., Speed, T.: Analysis of cDNA Microarray Images. Briefings in Bioinformatics 2(4), 341–349 (2001)CrossRefPubMedGoogle Scholar
  2. 2.
    Eisen, M.: ScanAlyze User Manual. Stanford University (1999)Google Scholar
  3. 3.
    Rueda, L., Qin, L.: A New Method for DNA Microarray Image Segmentation. In: Kamel, M.S., Campilho, A.C. (eds.) ICIAR 2005. LNCS, vol. 3656, pp. 886–893. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Adams, R., Bishof, L.: Seeded Region Growing. IEEE Trans. on Pattern Analysis and Machine Intelligence 16(6), 641–647 (1994)CrossRefGoogle Scholar
  5. 5.
    Talbot, B.: Regularized Seeded Region Growing. In: Proc. of the 6th International Symposium ISMM 2002, pp. 91–99 (2002)Google Scholar
  6. 6.
    Ahmed, A., Vias, M., Iyer, N., Caldas, C., Brenton, J.: Microarray Segmentation Methods Significantly Influence Data Precision. Nucleic Acids Research 32(5), e50 (2004)CrossRefGoogle Scholar
  7. 7.
    Angulo, J., Serra, J.: Automatic Analysis of DNA Microarray Images using Mathematical Morphology. Bioinformatics 19(5), 553–562 (2003)CrossRefPubMedGoogle Scholar
  8. 8.
    Rueda, L., Qin, L.: An Improved Clustering-Based Approach for DNA Microarray Image Segmentation. In: Campilho, A.C., Kamel, M.S. (eds.) ICIAR 2004. LNCS, vol. 3212, pp. 17–24. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Wu, S., Yan, H.: Microarray Image Processing Based on Clustering and Morphological Analysis. In: Proc. of the First Asia-Pacific Bioinformatics Conference on Bioinformatics, pp. 111–118 (2003)Google Scholar
  10. 10.
    Li, Q., Fraley, C., Bumgarner, R., Yeung, K., Raftery, A.: Donuts, Scratches and Blanks: Robust Model-Based Segmentation of Microarray Images. Technical Report No. 473, Department of Statistics, University of Washington (2005)Google Scholar
  11. 11.
    Maulik, U., Bandyopadhyay, S.: Performance Evaluation of Some Clustering Algorithms and Validity Indices. IEEE Trans. on Pattern Anal. Mach. Intell. 24(12), 1650–1654 (2002)CrossRefGoogle Scholar
  12. 12.
    Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 3rd edn. Academic Press, London (2006)Google Scholar
  13. 13.
    Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)Google Scholar
  14. 14.
    Veenman, C., Tax, D.: A Weighted Nearest Mean Classifier for Sparce Subspaces. Computer Vision and Pattern Recognition 2, 1171–1176 (2005)Google Scholar
  15. 15.
    Song, Y., Huang, J., Zhou, D., Zha, H., Lee, C.: IKNN: Informative K-Nearest Neighbor Pattern Classification. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 248–264. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  16. 16.
    Abe, S.: Support Vector Machines for Pattern Classification. Springer, Heidelberg (2005)Google Scholar
  17. 17.
    Dash, D., Cooper, G.: Exact Model Averaging with Naive Bayesian Classifiers. In: Proc. of the 19th International Conference on Machine Learning, pp. 91–98 (2002)Google Scholar
  18. 18.
    Baek, K., Draper, B., Beveridge, J., She, K.: PCA vs. ICA: A Comparison on the FERET Data Set. In: Proc. of the 4th Int. Conference on Computer Vision, Pattern Recognition and Image Processing, Durham, NC, pp. 824–827 (2002)Google Scholar
  19. 19.
    Safavian, S., Landgrebe, D.: A Survey of Decision Tree Classifier Methodology. IEEE Trans. on Systems, Man, Cybernetics, 660–674 (1991)Google Scholar
  20. 20.
    Gonzalez, R., Woods, R., Eddins, S.: Digital Image Processing Using Matlab. Prentice-Hall, Englewood Cliffs (2003)Google Scholar
  21. 21.
    Draghici, S.: Data Analysis Tools for DNA Microarrays. Chapman and Hall/CRC, Boca Raton (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Luis Rueda
    • 1
  • Juan Carlos Rojas
    • 2
  1. 1.School of Computer ScienceUniversity of WindsorWindsorCanada
  2. 2.Department of Computer ScienceUniversity of ConcepciónConcepciónChile

Personalised recommendations