Mammographic Segmentation and Density Classification: A Fractal Inspired Approach

  • Wenda He
  • Sam Harvey
  • Arne Juette
  • Erika R. E. Denton
  • Reyer Zwiggelaar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9699)

Abstract

Breast cancer is the most frequently diagnosed cancer in women. To date, the exact cause(s) of breast cancer still remains unknown. The most effective way to tackle the disease is early detection through breast screening programmes. Breast density is a well established image based risk factor. An accurate dense breast tissue segmentation can play a vital role in precise identification of women at risk, and determining appropriate measures for disease prevention. Fractal techniques have been used in many biomedical image processing applications with varying degrees of success. This paper describes a fractal inspired approach to mammographic tissue segmentation. A multiresolution stack representation and 3D histogram features (extended from 2D) are proposed. Quantitative and qualitative evaluation was performed including mammographic tissue segmentation and density classification. Results showed that the developed methodology was able to differentiate between breast tissue variations. The achieved density classification accuracy for 360 digital mammograms is 78 % based on the BI-RADS scheme. The developed fractal inspired approach in conjunction with the stack representation and 3D histogram features has demonstrated an ability to produce quality mammographic tissue segmentation. This in turn can be found useful in early breast cancer detection, risk-stratified screening, and aiding radiologists in the process of decision making prior to surgery and/or treatment.

Keywords

Fractal Mammographic tissue segmentation Mammographic density classification BI-RADS Tabár 

References

  1. 1.
    Lopes, R., Betrouni, N.: Fractal and multifractal analysis: a review. Med. Image Anal. 13(4), 634–649 (2009)CrossRefGoogle Scholar
  2. 2.
    Theiler, J.: Estimating fractal dimension. J. Opt. Soc. Am. A 7(6), 1055–1073 (1990)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Ciccotti, M., Mulargia, F.: Pernicious effect of physical cutoffs in fractal analysis. Phys. Rev. E 65, 037201 (2002)CrossRefGoogle Scholar
  4. 4.
    Napolitana, A., Ungania, S., Cannata, V. (eds.): Fractal dimension estimation methods for biomedical images. In: INTECH 2012 (2012)Google Scholar
  5. 5.
    Sarker, N., Chaudhuri, B.B.: An efficient differential box-counting approach to compute fractal dimension of image. IEEE Trans. Syst. Man Cybern. 24, 115–120 (1994)CrossRefGoogle Scholar
  6. 6.
    Dobrescu, R., Ichim, L., Crian, D.: Diagnosis of breast cancer from mammograms by using fractal measures. Int. J. Med. Imaging 1(2), 32–38 (2013)CrossRefGoogle Scholar
  7. 7.
    Zhang, P., Agyepong, K.: Wavelet-based fractal feature extraction for microcalcification detection in mammograms. In: IEEE SoutheastCon, pp. 147–150 (2010)Google Scholar
  8. 8.
    Oliver, A., Freixenet, J., Martí, R., Zwiggelaar, R.: A comparison of breast tissue classification techniques. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 872–879. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Oliver, A., Freixenet, J., Marti, R., Pont, J., Pérez, E., Denton, E.R.E., Zwiggelaar, R.: A novel breast tissue density classification methodology. IEEE Trans. Inf. Technol. Biomed. 12(1), 55–65 (2008)CrossRefGoogle Scholar
  10. 10.
    He, W., Juette, A., Denton, E.R.E., Oliver, A., Marti, R., Zwiggelaar, R.: A review on automatic mammographic density and parenchymal segmentation. Int. J. Breast Cancer, Article ID 276217 (2015)Google Scholar
  11. 11.
    Li, J., Du, Q., Sun, C.: An improved box-counting method for image fractaldimension estimation. Pattern Recogn. 42(11), 2460–2469 (2009)CrossRefMATHGoogle Scholar
  12. 12.
    Buczkowski, S., Kyriacos, S., Nekka, F., Cartilier, L.: The modified box-counting method: analysis of some characteristics parameters. Pattern Recogn. 3, 411–418 (1998)CrossRefMATHGoogle Scholar
  13. 13.
    Voss, R.F.: Random fractal forgeries. Fundam. Algorithms Comput. Graph. 17, 805–835 (1991)Google Scholar
  14. 14.
    Fernández-Martínez, M., Sánchez-Granero, M.A.: Fractal dimension for fractal structures. Topology Appl. 163, 93–111 (2014)MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    American College of Radiology: Breast Imaging Reporting, Data System BI-RADS, 5th edn. American College of Radiology, Reston (2013)Google Scholar
  16. 16.
    Tabár, L., Tot, T., Dean, P.B., Cancer, B.: The Art And Science Of Early Detection With Mamography: Perception, Interpretation, Histopatholigic Correlation, 1st edn. Georg Thieme, Stuttgart (2004)Google Scholar
  17. 17.
    He, W., Juette, A., Denton, E.R.E., Zwiggelaara, R.: Novel multiresolution mammographic density segmentation using pseudo 3D features, adaptive cluster merging. In: SPIE Proceedings: Medical Imaging, vol. 9413, pp. 94133I-1–94133I-6 (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Wenda He
    • 1
  • Sam Harvey
    • 2
  • Arne Juette
    • 3
  • Erika R. E. Denton
    • 3
  • Reyer Zwiggelaar
    • 1
  1. 1.Department of Computer ScienceAberystwyth UniversityAberystwythUK
  2. 2.School of Physics and AstronomyUniversity of ManchesterManchesterUK
  3. 3.Department of RadiologyNorfolk and Norwich University HospitalNorwichUK

Personalised recommendations