A New Preprocessing Filter for Digital Mammograms

  • Peyman Rahmati
  • Ghassan Hamarneh
  • Doron Nussbaum
  • Andy Adler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6134)


This paper presents a computer-aided approach to enhancing suspicious lesions in digital mammograms. The developed algorithm improves on a well-known preprocessor filter named contrast-limited adaptive histogram equalization (CLAHE) to remove noise and intensity inhomogeneities. The proposed preprocessing filter, called fuzzy contrast-limited adaptive histogram equalization (FCLAHE), performs non-linear enhancement. The filter eliminates noise and intensity inhomogeneities in the background while retaining the natural gray level variations of mammographic images within suspicious lesions. We applied Catarious segmentation method (CSM) to compare the segmentation accuracy in two scenarios: when there is no preprocessing filter, and when the proposed preprocessing filter is applied to the original image. The proposed filter has been evaluated on 50 real mammographic images and the experimental results show an average increase of segmentation accuracy by 14.16% when the new filter is applied.


Breast cancer mammography image denoising segmentation 


  1. 1.
    American Cancer Society, American Cancer Society: Breast Cancer Facts & Figures 2005- 2006, pp. 1–28 (2006)Google Scholar
  2. 2.
    Rangayyan, R.M.: Breast cancer and mammography. In: Neuman, M.R. (ed.) Biomedical Image Analysis, pp. 22–27. CRC Press, Boca Raton (2005)Google Scholar
  3. 3.
    Egan, R.L.: Breast Imaging: Diagnosis and Morphology of Breast Diseases. W. B. Saunders Co., Philadelphia (1988)Google Scholar
  4. 4.
    The Mosby Medical Encyclopedia, Revised edn. The Penguin Group, New York (1992)Google Scholar
  5. 5.
    Jeske, J.M., Bernstein, J.R., Stull, M.A.: Screening and Diagnostic Imaging. In: American Cancer Society Atlas of Clinical Oncology, pp. 41–63. B.C. Decker, London (2000)Google Scholar
  6. 6.
    Baeg, S., Kehtarnavaz, N.: Texture based classification of mass abnormalities in mammograms. In: Proc. of the 13th IEEE Symposium on Computer-Based Medical Systems (CBMS), Houston, TX, June 2000, vol. 1, pp. 163–168 (2000)Google Scholar
  7. 7.
    Mayo, P., Rodenas, F., Verdu, G.: Comparing methods to denoise mammographic images. In: Proc. of the 26th Annual Intl. Conference of the Engineering in Medicine and Biology Society (EMBC), vol. 1, pp. 247–250 (2004)Google Scholar
  8. 8.
    Pisano, E.D., Cole, E.B., Hemminger, B.M., Yaffe, M.J., Aylward, S.R., Maidment, A.D.A., Johnston, R.E., Williams, M.B., Niklason, L.T., Conant, E.F., Fajardo, L.L., Kopans, D.B., Brown, M.E., Pizer, S.M.: Image Processing Algorithms for Digital Mammography: A Pictorial Essay. RadioGraphics 20(5), 1479–1491 (2000)Google Scholar
  9. 9.
    Mekle, R., Laine, A.F., Smith, S., Singer, C., Koenigsberg, T., Brown, M.: Evaluation of a multiscale enhancement protocol for digital mammography. In: Proc. of the Wavelet Applications in Signal and Image Processing VIII, San Diego, CA, USA, vol. 4119, pp. 1038–1049 (2000)Google Scholar
  10. 10.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall, Upper Saddle River (2002)Google Scholar
  11. 11.
    Mayo, P., Rodenas, F., Verdu, G.: Comparing methods to denoise mammographic images. In: Proc. of the 26th Annual Intl. Conference of the Engineering in Medicine and Biology Society (EMBC), vol. 1, pp. 247–250 (2004)Google Scholar
  12. 12.
    Guliato, D., Rangayyan, R.M., Carnielli, W.A., Zuffo, J.A., Desautels, J.E.L.: Segmetation of breast tumors in mammograms using fuzzy sets. Journal of Electronic Imaging 12(3), 369–378 (2003)CrossRefGoogle Scholar
  13. 13.
    Heath, M., Bowyer, K., Kopans, D., Kegelmeyer, W.P.H., Moore, R., Chang, K., MunishKumaran, S.: Current status of the Digital Database for Screening Mammography (accessed September 15, 2009)
  14. 14.
    Catarious, D.M., Baydush, A.H., Floyd, C.E.: Incorporation of an iterative, linear segmentation routine into a mammographic mass CAD system. Medical Physics 31(6), 1512–1520 (2004)CrossRefGoogle Scholar
  15. 15.
    Rahmati, P., Ayatollahi, A.: Maximum Likelihood Active Contours Specialized for Mammography Segmentation. In: The 2nd IEEE International Conference on BioMedical Engineering and Informatics (BMEI’09), China, vol. 1, pp. 257–260 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Peyman Rahmati
    • 1
  • Ghassan Hamarneh
    • 2
  • Doron Nussbaum
    • 3
  • Andy Adler
    • 1
  1. 1.Dept. of System and computer EngineeringCarleton UniversityCanada
  2. 2.School of Computing ScienceSimon Fraser UniversityCanada
  3. 3.Dept. of Computer ScienceCarleton UniversityCanada

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