Recent Advances in Cell Classification for Cancer Research and Drug Discovery

  • Dat T. Tran
  • Tuan Pham
Part of the Applied Bioinformatics and Biostatistics in Cancer Research book series (ABB)


Drug effects on cancer cells are investigated through measuring cell cycle progression in individual cells as a function of time. This investigation requires the processing and analysis of huge amounts of image data obtained in time-lapse microscopy. Manual image analysis is very time consuming thus costly, potentially inaccurate, and poorly reproducible. Stages of an automated cellular imaging analysis consist of segmentation, feature extraction, classification, and tracking of individual cells in a dynamic cellular population. The feature extraction and classification of cell phases are considered the most difficult tasks of such analysis. We review several techniques for feature extraction and classification. We then present our work on an automated feature weighting technique for feature selection and combine this technique with cellular phase modeling techniques for classification. These combined techniques perform the two most difficult tasks at the same time and enhance the classification performance. Experimental results have shown that the combined techniques are effective and have potential for higher performance.


Hide Markov Model Gaussian Mixture Model Cell Phase Vector Quantization Fuzzy Measure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by the ARC under project DP0665598 to T. Pham.


  1. Giuliano, K.A., Haskins, J.R., and Taylor, D.L.: Advances in high content screening for drug discovery. In: ASSAY and Drug Development Technologies, vol. 1, no. 4, pp. 565–577 (2003)CrossRefGoogle Scholar
  2. Abraham, V.C., Taylor, D.L., and Haskins, J.R.: High content screening applied to large-scale cell biology. In: Trends in Biotechnology, Elsevier, vol. 22, no. 1, pp. 15–23 (2004)Google Scholar
  3. Dunkle, R.: Role of image informatics in accelerating drug discovery and development. In: Drug Discovery World, vol. 7, pp. 7–11 (2002)Google Scholar
  4. Fox, S.: Accommodating cells in HTS. In: Drug Discovery World, vol. 5, pp. 21–30 (2003)Google Scholar
  5. Feng, Y.: Practicing cell morphology based screen. In: European Pharmaceutical Review, vol. 7, pp. 75–82 (2002)Google Scholar
  6. Yarrow, J.C., et al.: Phenotypic screening of small molecule libraries by high throughput cell imaging. In: Comb Chem High Throughput Screen, vol. 6, pp. 279–286 (2003)Google Scholar
  7. Murphy, D.B.: Fundamentals of light Microscopy and Electronic Imaging, Wiley-Liss (2001)Google Scholar
  8. Hiraoka, Y. and Haraguchi, T.: Fluoresence imaging of mammalian living cells. In: Chromosome Res, vol. 4, pp. 173–176 (1996)CrossRefGoogle Scholar
  9. Kanda, T., Sullivan, K.F., and Wahl, G.M.: Histone-GFP fusion protein enables sensitive analysis of chromosome dynamics in living mammalian cells. In: Current Biology, vol. 8, pp. 377–385 (1998)CrossRefGoogle Scholar
  10. Chen, X., Zhou, X., and Wong, S.T.C.: Automated segmentation, classification, and tracking cancer cell nuclei in time-lapse microscopy. In: IEEE Trans. on Biomedical Engineering, vol. 53, no. 4, pp. 762–766 (2006)CrossRefGoogle Scholar
  11. Wang, M., Zhou, X., King, R.W., and Wong, S.T.: Context based mixture model for cell phase identification in automated fluorescence microscopy. In: BMC Bioinformatics vol. 8, no. 32 (2007)Google Scholar
  12. MacAulay, C. and Palcic, B.A.: Comparison of some quick and simple threshold selection methods for stained cells. In: Anal. Quant. Cytol. Histol., vol. 10, pp. 134–138 (1988)Google Scholar
  13. Bleau A. and Leon J.L.: Watershed-based segmentation and region merging. In: Computer Vision and Image Understanding, vol. 77, pp. 317–370 (2000)CrossRefGoogle Scholar
  14. Umesh, A.P.S. and Chaudhuri B.B.: An efficient method based on watershed amd rule-based merging for segmentation of 3-D histopathological images. In: Pattern Recognition, vol. 34, pp. 1449–1458 (2001)CrossRefGoogle Scholar
  15. Ma, Z., Tavares, J.M.R.S., and Jorge, R.N.: Segmentation of structures in medical images: review and a new computational framework. In: the Eigth International Symposium on Computer Methods in Biomechanics and Biomedical Engineering, Portugal (2008)Google Scholar
  16. Canny, J.: A computational approach to edge detection. In: IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 8, pp. 679–714 (1986)CrossRefGoogle Scholar
  17. Adams, R. and Bischof L., Seeded region growing. In: IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 16, pp. 641–47 (1994)CrossRefGoogle Scholar
  18. Pohle, R. and Toennies, K.D.: Segmentation of medical images using adaptive region growing. In: SPIE, pp. 1337–1346 (2001)Google Scholar
  19. Bell, A., Herberich, G., Dietrich, M.-E., Bocking, A., and Aach, T.: Analysis of silver stained cell specimens: nuclear segmentation and validation. In: International Conference on Medical Imaging (2008)Google Scholar
  20. Norberto M., Andres S., Carlos Ortiz S. Juan Jose V., Francisco P., and Jose Miguel G.: Applying watershed algorithms to the segmentation of clustered nuclei. In: Cytometry, vol. 28, pp. 289–297 (1997)Google Scholar
  21. Otsu N.: A threshold selection method from gray level histogram. In: IEEE Trans. System, Man, and Cybernetics, vol. 8, pp. 62–66 (1978)Google Scholar
  22. Bleau A. and Leon, J.L.: Watershed-based segmentation and region merging. In: Computer Vision and Image Understanding, pp. 317–370 (2000)Google Scholar
  23. Furui, S.: Recent advances in speaker recognition. In: Patter Recognition Letter, vol. 18, pp. 859–872 (1997)CrossRefGoogle Scholar
  24. Juang, B.-H.: The past, present, and future of speech processing. In: IEEE Signal Processing Magazine, vol. 15, no. 3, pp. 24–48 (1998)CrossRefGoogle Scholar
  25. Kulkarni, V.G.: Modeling and Analysis of Stochastic Systems. Chapman & Hall, UK (1995)Google Scholar
  26. Rabiner, L.R. and Juang, B.H.: Fundamentals of Speech Recognition. Prentice Hall PTR, USA (1993)Google Scholar
  27. Tran, D. and Wagner, M.: Generalised Fuzzy hidden Markov models for speech recognition. In: Lecture Notes in Computer Science: Advances in Soft Computing - AFSS 2002, N.R. Pal and M. Sugeno (Eds.), pp. 345–351, Springer-Verlag (2002)Google Scholar
  28. Tran, D.T. and Pham, T: Modeling methods for cell phase classification, Book chapter in the book Advanced Computational Methods for Biocomputing and Bioimaging, Editors: Tuan Pham, Hong Yan, and Denis I. Crane, Nova Science Publishers, New York, USA, ISBN: 1–60021–278–6, chapter ??, pp. 143–166 (2007)Google Scholar
  29. Tran, D., Pham, T., and Zhou, X.: Subspace vector quantization and Markov modeling for cell phase classification. In: Proceedings of the International Conference on Image Analysis and Recognition (ICIAR), in Image Analysis and Recognition of Lecture Notes in Computer Science, Portugal, vol. 5112, pp. 844–853 (2008)Google Scholar
  30. Dempster, A.P., Laird, N.M., and Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. In: Journal of the Royal Statistical Society, Ser. B, 39: pp. 1–38 (1997)Google Scholar
  31. Tran, D. and Wagner, M.: Fuzzy Gaussian mixture models for speaker recognition. In: special issue of the Australian Journal of Intelligent Information Processing Systems (AJIIPS), vol. 5, no. 4, pp. 293–300 (1998)Google Scholar
  32. Tran, D. and Pham, T.: Automated feature weighting-based cell phase classification. In: Proc. IASTED International Symposium on Computational Biology and Bioinformatics, USA, pp. 274–277 (2008)Google Scholar
  33. Pham, T., Tran, D., and Zhou, X.: Fuzzy information fusion of classification models for high-throughput image screening of cancer cells in time-lapse microscopy. In: KES Journal, vol. 11, no. 4, pp. 237–246, IOS Press (2007)Google Scholar
  34. Sugeno, M.: Fuzzy measures and fuzzy integrals: a survey. In: M.M. Gupta, G.N. Saridis, and B.R. Gaines, eds, Fuzzy Automata and Decision Processes, North-Holland, New York, pp. 89–102 (1977)Google Scholar
  35. Choquet, G.: Theory of capacities. In: Annales de l’Institut Fourier, vol. 5, pp. 131–295 (1953)Google Scholar
  36. Pham, T., Tran, D.T., Zhou, X., and Wong, S.T.C.: Integrated algorithms for image analysis and identification of nuclear division for high-content cell-cycle screening. In: Internation Journal of Computational Intelligence and Applications, vol. 6, pp. 21–43 (2006)CrossRefGoogle Scholar
  37. Pham, T., Tran, D.T., Zhou, X., and Wong, S.T.C.: A microscopic image classification system for high-throughput cell-cycle screening. In: Proceeding of International Journal Intelligent Computing in Medical Sciences and Image Processing, vol. 1, no. 1, pp. 67–77 (2007)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.University of CanberraCanberraAustralia

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