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Database Supported Fine Needle Biopsy Material Diagnosis Routine

  • Maciej Hrebień
  • Józef Korbicz
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 57)

Abstract

This paper describes cytological image segmentation and diagnosis method. The analysis includes an expert database supported Hough transform for irregular structures, image pre-processing and pre-segmentation, nuclei features extraction and final diagnosis stage. One can also find here experimental results collected on a hand-prepared benchmark database that show the quality of the proposed method for typical and non-typical cases.

Keywords

Breast Cancer Diagnosis Human Expert Segmentation Stage Fourier Descriptor Fine Needle Biopsy 
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.

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References

  1. 1.
    Ballard, D.: Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13(2), 111–122 (1981)CrossRefzbMATHGoogle Scholar
  2. 2.
    Boldrini, J., Costa, M.: An application of optimal control theory to the design of theoretical schedules of anticancer drugs. Int. Journal of Applied Mathematics and Computer Science 9(2), 387–399 (1999)Google Scholar
  3. 3.
    Dinh, N., Osowski, S.: Shape recognition using FFT preprocessing and neural network. Compel 17(5/6), 658–666 (1998)Google Scholar
  4. 4.
    Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. John Wiley & Sons, Chichester (2001)Google Scholar
  5. 5.
    Hrebien, M., Korbicz, J., Obuchowicz, A.: Hough transform, (1+1) search strategy and watershed algorithm in segmentation of cytological images. In: Proc. of the 5th Int. Conf. on Computer Recognitions Systems CORES 2007, Wrocław, Poland. Advances in Soft Computing, pp. 550–557. Springer, Berlin (2007)Google Scholar
  6. 6.
    Jelen, L., Fevens, T., Krzyzak, A.: Classification of breast cancer malignancy using cytological images of fine needle aspiration biopsies. Int. Journal of Applied Mathematics and Computer Science 18(1), 75–83 (2008)Google Scholar
  7. 7.
    Kimmel, M., Lachowicz, M., Swierniak, A. (eds.): Cancer growth and progression, mathematical problems and computer simulations. Int. Journal of Applied Mathematics and Computer Science 13(3) (2003) (special issue)Google Scholar
  8. 8.
    Lee, M., Street, W.: Dynamic learning of shapes for automatic object recognition. In: Proc. of the 17th Workshop Machine Learning of Spatial Knowledge, Stanford, USA, pp. 44–49 (2000)Google Scholar
  9. 9.
    Marciniak, A., Obuchowicz, A., Monczak, R., Kolodzinski, M.: Cytomorphometry of fine needle biopsy material from the breast cancer. In: Proc. of the 4th Int. Conf. on Computer Recognition Systems CORES 2005, Rydzyna, Poland. Advances in Soft Computing, pp. 603–609. Springer, Berlin (2005)Google Scholar
  10. 10.
    Obuchowicz, A., Hrebien, M., Nieczkowski, T., Marciniak, A.: Computational intelligence techniques in image segmentation for cytopathology. In: Smolinski, T., Milanova, M., Hassanien, A. (eds.) Computational Intelligence in Biomedicine and Bioinformatics, pp. 169–199. Springer, Berlin (2008)CrossRefGoogle Scholar
  11. 11.
    Pena-Reyes, C., Sipper, M.: Envolving fuzzy rules for breast cancer diagnosis. In: Proc. of the Int. Symposium on Nonlinear Theory and Application, vol. 2, pp. 369–372. Polytechniques et Universitaires Romandes Press (1998)Google Scholar
  12. 12.
    Pratt, W.: Digital Image Processing. John Wiley & Sons, New York (2001)CrossRefGoogle Scholar
  13. 13.
    Russ, J.: The Image Processing Handbook. CRC Press, Boca Radon (1999)zbMATHGoogle Scholar
  14. 14.
    Setiono, R.: Extracting rules from pruned neural networks for breast cancer diagnosis. Artificial Intelligence in Medicine 8(1), 37–51 (1996)CrossRefGoogle Scholar
  15. 15.
    Swierniak, A., Ledzewicz, U., Schattler, H.: Optimal control for a class of compartmental models in cancer chemotherapy. Int. Journal of Applied Mathematics and Computer Science 13(3), 357–368 (2003)zbMATHMathSciNetGoogle Scholar
  16. 16.
    Tadeusiewicz, R.: Vision Systems of Industrial Robots. WNT, Warszawa (1992) (in Polish)Google Scholar
  17. 17.
    Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. on Pattern Analysis and Machine Intelligence 13(6), 583–598 (1991)CrossRefGoogle Scholar
  18. 18.
    Wolberg, W., Street, W., Mangasarian, O.: Breast cytology diagnosis via digital image analysis. Analytical and Quantitative Cytology and Histology 15(6), 396–404 (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Maciej Hrebień
    • 1
  • Józef Korbicz
    • 1
  1. 1.Institute of Control and Computation EngineeringUniversity of Zielona GóraZielona GóraPoland

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