Comparison of Pleomorphic and Structural Features Used for Breast Cancer Malignancy Classification

  • Łukasz Jeleń
  • Adam Krzyżak
  • Thomas Fevens
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5032)


Malignancy of a cancer is one of the most important factors that are taken into consideration during breast cancer. Depending on the malignancy grade the appropriate treatment is suggested. In this paper we make use of the Bloom-Richardson grading system, which is widely used by pathologists when grading breast cancer malignancy. Here we discuss the use of two categories of cells features for malignancy classification. The features are divided into polymorphic features that describe nuclei shapes, and structural features that describe cells ability to form groups. Results presented in this work, show that calculated features present a valuable information about cancer malignancy and they can be used for computerized malignancy grading. To support that argument classification error rates are presented that show the influence of the features on classification. In this paper we compared the performance of Support Vector Machines (SVMs) with three other classifiers. The SVMs presented here are able to assign a malignancy grade based on pre–extracted features with accuracy up to 94.24% for pleomorphic features and with an accuracy 91.33% when structural features were used.


malignancy grading FNA grading breast cancer grading Bloom–Richardson features 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Breast Cancer Society of Canada,
  2. 2.
    Bloom, H., Richardson, W.: Histological Grading and Prognosis in Breast Cancer. Br. J. Cancer 11, 359–377 (1957)Google Scholar
  3. 3.
    Le Doussal, V., Tubiana-Hulin, M., Friedman, S., Hacene, K., Spyratos, F., Brunet, M.: Prognostic value of histologic grade nuclear components of scarff–bloom–richardson (sbr). an improved score modification based on a multivariate analysis of 1262 invasive ductal breast carcinomas. Cancer 64(9) (1914)Google Scholar
  4. 4.
    Ridler, T., Calvard, S.: Picture thresholding using an iterative selection. IEEE Trans. System, Man and Cybernetics 8, 630–632 (1978)CrossRefGoogle Scholar
  5. 5.
    Li, C., Xu, C., Gui, C., Fox, M.: Level Set Evolution Without Re-initialization: A New Variational Formulation. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition 2005, pp. 430–436 (2005)Google Scholar
  6. 6.
    Ballard, H.: Generalizing the Hough Transform to Detect Arbitrary Shapes. Pattern Recognition 13(2), 111–122 (1981)zbMATHCrossRefGoogle Scholar
  7. 7.
    Street, W.N., Wolberg, W.H., Mangasarian, O.L.: Nuclear Feature Extraction for Breast Tumor Diagnosis. In: IS&T/SPIE 1993 Int. Symp. Elec. Img., San Jose, California, vol. 1905, pp. 861–870 (1993)Google Scholar
  8. 8.
    Lee, K., Street, W.: Generalized Hough Transforms with Flexible Templates. In: Proc. ICAI, Las Vegas, NV, vol. 3, pp. 1133–1139 (2000)Google Scholar
  9. 9.
    Jeleń, Ł., Krzyżak, A., Fevens, T.: Automated Feature Extraction for Breast Cancer Grading with Bloom-Richardson Scheme. Int. J. CARS 1(1), 468–469 (2006)Google Scholar
  10. 10.
    Droske, M., Meyer, B., Rumpf, M., Schaller, K.: An adaptive Level Set Method for Medical Image Segmentation. In: Insana, M.F., Leahy, R.M. (eds.) IPMI 2001. LNCS, vol. 2082, pp. 416–422. Springer, Heidelberg (2001)Google Scholar
  11. 11.
    Deng, J., Tsui, H.: A fast level set method for segmentation of low contrast noisy biomedical images. Pattern Recognition Letters 23(1-3), 161–169 (2002)zbMATHCrossRefGoogle Scholar
  12. 12.
    Tsai, A., Yezzi, A., Wells III, W., Tempany, C., Tucker, D., Fan, A., Grimson, W., Willsky, A.: A shape-based approach to the segmentation of medical imagery using level sets. Medical Imaging 22(2), 137–154 (2003)CrossRefGoogle Scholar
  13. 13.
    Li, S., Fevens, T., Krzyżak, A., Jin, C., Li, S.: Fast and robust clinical triple-region image segmentation using one level set function. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 766–773. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    Osher, S., Sethian, J.: Fronts Propagating with Curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. J. Comp. Phys. 79, 12–49 (1988)zbMATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Sethian, J., Adalsteinsson, D.: An Overview of Level Set Methods for Etching, Deposition, and Lithography Development. IEEE Trans. Semiconductor Manufacturing 10(1), 167–184 (1997)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Jeleń, Ł., Fevens, T., Krzyżak, A.: Classification of Breast Cancer Malignancy using Cytological Images of Fine Needle Aspiration Biopsies. J. AMCS 18(1) (in press, 2008)Google Scholar
  17. 17.
    Zunic, J., Rosin, P.: A Convexity Measurement for Polygons. British Machine Vision Conference 24, 173–182 (2002)Google Scholar
  18. 18.
    Friess, T., Cristianini, N., Campbell, C.: The kernel adatron algorithm: a fast and simple learning procedure for support vector machines. In: 15th International Conference on Machine Learning, Morgan Kaufman Publishers, San Francisco (1998)Google Scholar
  19. 19.
    Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. Wiley Interscience Publishers, New York (2000)Google Scholar
  20. 20.
    Kohonen, T.: The self–organizing map. Proc IEEE 78, 1464–1480 (1990)CrossRefGoogle Scholar
  21. 21.
    Oja, E.: A simplified neuron modeled as a principal component analyzer. J. Math. Biol. 15, 267–273 (1982)zbMATHCrossRefMathSciNetGoogle Scholar
  22. 22.
    Bradley, A.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30(7), 1145–1159 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Łukasz Jeleń
    • 1
  • Adam Krzyżak
    • 1
  • Thomas Fevens
    • 1
  1. 1.Department of Computer Science and Software EngineeringConcordia UniversityMontrealCanada

Personalised recommendations