A Hybrid Approach for Pap-Smear Cell Nucleus Extraction

  • M. Orozco-Monteagudo
  • Hichem Sahli
  • Cosmin Mihai
  • A. Taboada-Crispi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6718)

Abstract

This paper, proposes a two-phases approach for a computer-assisted screening system that aims at early diagnosis of cervical cancer in Pap smear images and accurate segmentation of nuclei. The first phase uses spectral, shape as well as the class membership to produce a nested hierarchical partition (hierarchy of segmentations). The second phase, selects the best hierarchical level based on an unsupervised criterion, and refines the obtained segmentation by classifying the individual regions using a Support Vector Machine (SVM) classifier followed by merging adjacent regions belonging to the same class. The effectiveness of the proposed approach for producing a better separation of nucleus regions and cytoplasm areas is demonstrated using both ground truth data, being manually segmented images by pathologist experts, and comparison with state-of-art methods.

Keywords

microscopic images cell segmentation watershed SVM classification 

References

  1. 1.
    Papanicolaou, G.: A new procedure for staining vaginal smears. Science 95, 438–439 (1942)CrossRefGoogle Scholar
  2. 2.
    Pantanowitz, L., Hornish, M., Goulart, R.: The impact of digital imaging in the field of cytopathology. Cytojournal 6(1), 6–15 (2010)CrossRefGoogle Scholar
  3. 3.
    Ricketts, I., Banda-Gamboa, H., Cairns, A., Hussein, K.: Automatic classification of cervical cells-using the frequency domain. In: IEEE Colloquium on Applications of Image Processing in Mass Health Screening, IET, p. 9 (2002)Google Scholar
  4. 4.
    Walker, R., Jackway, P.: Statistical geometric features extensions for cytological texture analysis. In: Proceedings of 13th International Conference on Pattern Recognition, vol. 2, pp. 790–794 (1996)Google Scholar
  5. 5.
    Lezoray, O., Cardot, H.: Cooperation of color pixel classification schemes and color watershed: a study for microscopic images. IEEE transactions on Image Processing 11, 783–789 (2002)CrossRefGoogle Scholar
  6. 6.
    Bak, E., Najarian, K., Brockway, J.: Efficient segmentation framework of cell images in noise environments. In: 26th IEEE Annual International Conference on Engineering in Medicine and Biology Society (IEMBS 2004), vol. 1, pp. 1802–1805 (2005)Google Scholar
  7. 7.
    Beucher, S.: Watershed, hierarchical segmentation and waterfall algorithm. Mathematical morphology and its applications to image processing, 69–76 (1994)Google Scholar
  8. 8.
    Roerdink, J., Meijster, A.: The watershed transform: Definitions, algorithms and parallelization strategies. Mathematical morphology 187 (2000)Google Scholar
  9. 9.
    DiZenzo, S.: A note on the gradient of a multi-image. Comput. Vision, Graphics. Image Proc. 33(1), 116–125 (1986)CrossRefGoogle Scholar
  10. 10.
    Geerinck, T., Sahli, H., Henderickx, D., Vanhamel, I., Enescu, V.: Modeling attention and perceptual grouping to salient objects. In: Paletta, L., Tsotsos, J.K. (eds.) WAPCV 2008. Lecture Notes in Computer Science(LNAI), vol. 5395, pp. 166–182. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  11. 11.
    Lucchi, A., Smith, K., Achanta, R., Lepetit, V., Fua, P.: A Fully Automated Approach to Segmentation of Irregularly Shaped Cellular Structures in EM Images. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2010, pp. 463–471 (2010)Google Scholar
  12. 12.
    Calderero, F., Marques, F.: General region merging approaches based on information theory statistical measures. In: 15th IEEE International Conference on Image Processing, ICIP 2008, pp. 3016–3019 (2008)Google Scholar
  13. 13.
    Platt, J.C.: Probabilistic outputs for support vector machines and comparison to regularized likelihood methods. In: Advances in large margin classifiers, pp. 61–74 (1999)Google Scholar
  14. 14.
    Cristianini, N., Shawe-Taylor, J.: Introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press, Cambridge (2000)Google Scholar
  15. 15.
    Borsotti, M., Campadelli, P., Schettini, R.: Quantitative evaluation of color image segmentation results. Pattern Recognition Letters 19(8), 741–747 (1998)CrossRefMATHGoogle Scholar
  16. 16.
    Jennrich, R., Sampson, P.: Stepwise discriminant analysis. In: Mathematical methods for digital computers, pp. 339–358 (1960)Google Scholar
  17. 17.
    Canu, S., Grandvalet, Y., Rakotomamonjy, A.: SVM and Kernel Methods MATLAB Toolbox. Perception de Syst émes et Information, INSA de Rouen, France (2003)Google Scholar
  18. 18.
    Joshi, M.V.: On evaluating performance of classifiers for rare classes. In: Proceedings of the IEEE International Conference on Data Mining ICDM 2002, p. 641. IEEE Computer Society, Washington (2002)Google Scholar
  19. 19.
    Vanhamel, I., Mihai, C., Sahli, H., Katartzis, A., Pratikakis, I.: Scale Selection for Compact Scale-Space Representation of Vector-Valued Images. International Journal of Computer Vision 84(2), 194–204 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • M. Orozco-Monteagudo
    • 1
  • Hichem Sahli
    • 2
  • Cosmin Mihai
    • 2
  • A. Taboada-Crispi
    • 1
  1. 1.Universidad Central de Las VillasCuba
  2. 2.Electronics and Informatics Dept.Vrije Universiteit Brussel - ETROBrusselsBelgium

Personalised recommendations