Advertisement

Subspace Vector Quantization and Markov Modeling for Cell Phase Classification

  • Dat Tran
  • Tuan Pham
  • Xiaobo Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5112)

Abstract

Vector quantization (VQ) and Markov modeling methods for cellular phase classification using time-lapse fluorescence microscopic image sequences have been proposed in our previous work. However the VQ method is not always effective because cell features are treated equally although their importance may not be the same. We propose a subspace VQ method to overcome this drawback. The proposed method can automatically weight cell features based on their importance in modeling. Two weighting algorithms based on fuzzy c-means and fuzzy entropy clustering are proposed. Experimental results show that the proposed method can improve the cell phase classification rates.

Keywords

Fluorescence microscopic imaging cell phase classification subspace vector quantization fuzzy c-means fuzzy entropy Markov chain 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Fox, S.: Accommodating cells in HTS. Drug Discovery World 5, 21–30 (2003)Google Scholar
  2. 2.
    Feng, Y.: Practicing cell morphology based screen. European Pharmaceutical Review 7, 7–11 (2002)Google Scholar
  3. 3.
    Dunkle, R.: Role of image informatics in accelerating drug discovery and development. Drug Discovery World 5, 75–82 (2003)Google Scholar
  4. 4.
    Yarrow, J.C., et al.: Phenotypic screening of small molecule libraries by high throughput cell imaging. Comb. Chem. High Throughput Screen 6, 279–286 (2003)Google Scholar
  5. 5.
    Murphy, D.B.: Fundamentals of light Microscopy and Electronic Imaging. Wiley-Liss, Chichester (2001)Google Scholar
  6. 6.
    Hiraoka, Y., Haraguchi, T.: Fluoresence imaging of mammalian living cells. Chromosome Res. 4, 173–176 (1996)CrossRefGoogle Scholar
  7. 7.
    Kanda, T., Sullivan, K.F., Wahl, G.M.: Histone-GFP fusion protein enables sensitive analysis of chromosome dynamics in living mammalian cells. Current Biology 8, 377–385 (1998)CrossRefGoogle Scholar
  8. 8.
    Chen, X., Zhou, X., Wong, S.T.C.: Automated segmentation, classification, and tracking cancer cell nuclei in time-lapse microscopy. IEEE Trans. on Biomedical Engineering (in press)Google Scholar
  9. 9.
    Tran, D.T., Pham, T.D.: Modeling Methods for Cell Phase Classification. In: Pham, T.D., Yan, H., Crane, D.I. (eds.) Advanced Computational Methods for Biocomputing and Bioimaging, ch. 7, pp. 143–166. Nova Science Publishers, New York (2007)Google Scholar
  10. 10.
    Pham, T.D., Tran, D.T., Zhou, X., Wong, S.T.C.: Classification of Cell Phases in Time-Lapse Images by Vector Quantization and Markov Models. In: Greer, E.V. (ed.) Neural Stem Cell Research, pp. 155–174. Nova Science Publishers, New York (2006)Google Scholar
  11. 11.
    Duda, R.O., Hart, P.E.: Pattern classification and scene analysis. John Wiley & Sons, New York (1973)zbMATHGoogle Scholar
  12. 12.
    Huang, J.Z., Ng, M.K., Rong, H., Li, Z.: Automated Variable Weighting in k-means Type Clustering. Trans. Pattern Analysis and Machine Intelligence 27(5), 657–668Google Scholar
  13. 13.
    Special Issue on: Molecular Imaging: Emerging Technology & Biomedical Applications, Proceedings of the IEEE 93(4) (2005)Google Scholar
  14. 14.
    Paliwal, K.K., Rao, P.V.S.: Application of k-nearest-neighbor decision rule in vowel recognition. IEEE Trans. Pattern Analysis and Machine Intelligence 5, 229–231 (1983)CrossRefGoogle Scholar
  15. 15.
    Zhou, X., Chen, X., King, R., Wong, S.T.C.: Time-lapse cell cycle quantitative data analysis using Gaussian mixture models. In: Wong, S.T.C., Li, C.S. (eds.) Life Science Data Mining. World Scientific, Singapore (in press)Google Scholar
  16. 16.
    Tran, D., Wagner, M.: Fuzzy entropy clustering. In: Proc. FUZZ-IEEE 2000 Conf., vol. 1, pp. 152–157 (2000)Google Scholar
  17. 17.
    Ginneken, B.V., Loog, M.: Pixel position regression - application to medical image segmentation. In: Proc. 17th Int. Conf. Pattern Recognition ICPR2004, vol. 3, pp. 718–721 (2004)Google Scholar
  18. 18.
    Tokola, T., Pitkänen, J., Partinen, S., Muinonen, E.: Point accuracy of a non-parametric method in estimation of forest characteristics with different satellite materials. International Journal of Remote Sensing 17, 2333–2351 (1996)CrossRefGoogle Scholar
  19. 19.
    Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Bostein, D., Altman, R.B.: Missing value estimation methods for DNA microarrays. Bioinformatics 17, 520–525 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Dat Tran
    • 1
  • Tuan Pham
    • 2
  • Xiaobo Zhou
    • 3
  1. 1.Faculty of Information Sciences and EngineeringUniversity of CanberraAustralia
  2. 2.Bioinformatics Applications Research CentreJames Cook UniversityAustralia
  3. 3.HCNR-Center for BioinformaticsHarvard Medical SchoolBostonUSA

Personalised recommendations