Advertisement

Automated Recognition of Cellular Phenotypes by Support Vector Machines with Feature Reduction

  • Y. Mao
  • Z. Xia
  • D. Pi
  • X. Zhou
  • Y. Sun
  • S. T. C. Wong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4251)

Abstract

In this paper, wrapper based feature selection by support vector machine is used for cellular multi-phenotypic mitotic analysis (MMA) in high content screening (HCS). Haralick texture feature subset and Zernike polynomial moment subset are used respectively or combined together as extracted digital feature set for original cellular images. Feature reduction is done by support vector machine based recursive feature elimination algorithm on these feature sets. With optimal feature subset selected, fuzzy support vector machine are adopted to judge the cellular phenotype. The results indicate Haralick texture feature subset is complementary with Zernike polynomial moment subset, when these two feature subsets are combined together; the cellular phase identification system achieved 99.17% accuracy, which is better than only one feature subset of them is used. The recognition accuracy with feature reduction is better than that achieved when no feature reduction done or using PCA as feature recombination tool on these datasets.

Keywords

Support Vector Machine Recognition Accuracy Feature Subset Feature Reduction Cellular Phenotype 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Perlman, Z., Slack, M., Feng, Y., Mitchison, T., Wu, L., Altsschule, S.: Multidimensional drug profiling by automated microscopy. Science 306, 1194–1198 (2004)CrossRefGoogle Scholar
  2. 2.
    Zhou, X., Cao, X., Perlman, Z., Wong, S.: A computerized cellular imaging system for high content analysis in Monastrol suppressor screens. Journal of Biomedical informatics (in press, 2005)Google Scholar
  3. 3.
    Huang, K., Velliste, M., Murphy, R.: Feature reduction for improved recognition of subcellular location patterns in fluorescence microscope images. In: Proc. SPIE, vol. 4962, pp. 307–318 (2003)Google Scholar
  4. 4.
    Boland, M., Murphy, R.: A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of hela cells. Bioinformatics 17, 1213–1223 (2001)CrossRefGoogle Scholar
  5. 5.
    Teague, M.: Image analysis via the general theory of moments. Journal of the optical society of America 70, 920–930 (1980)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Haralick, R., Shapiro, L.: Computer and robot vision. Addison-Wesley, Reading (1992)Google Scholar
  7. 7.
    Khotanzad, A., Hong, Y.: Rotation invariant image recognition using features selected via a systematic method. Pattern recognition 23, 1089–1101 (1990)CrossRefGoogle Scholar
  8. 8.
    Boland, M., Markey, M., Murphy, R.: Automated recognition of patterns characteristic of subcellular structures in fluorescence microscopy images. Cytometry 22, 366–375 (1998)CrossRefGoogle Scholar
  9. 9.
    Mao, Y., Zhou, X., Pi, D., Wong, S., Sun, X.: Multi-class cancer classification by using fuzzy support vector machine and binary decision tree with gene selection. Journal of Biomedicine and Biotechnology 2, 160–171 (2005)CrossRefGoogle Scholar
  10. 10.
    Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Machine learning 46, 389–422 (2002)MATHCrossRefGoogle Scholar
  11. 11.
    Vapnik, V.: The nature of statistical learning theory. Springer, New York (2000)MATHGoogle Scholar
  12. 12.
    Abe, S., Inoue, T.: Fuzzy support vector machines for multiclass problems. In: European Symposium on Artificial Neural Networks, Bruges, Belgium (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Y. Mao
    • 1
  • Z. Xia
    • 1
  • D. Pi
    • 1
  • X. Zhou
    • 2
  • Y. Sun
    • 1
  • S. T. C. Wong
    • 2
  1. 1.National Laboratory of Industrial Control Technology, Institute of Industrial Process ControlZhejiang UniversityHangzhouP.R. China
  2. 2.Harvard Center for Neurodegeneration and Repair, Harvard Medical School and Brigham and Women’s Hospital, Harvard Medical SchoolHarvard UniversityBostonUSA

Personalised recommendations