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Adaptive Local Contrast Enhancement Combined with 2D Discrete Wavelet Transform for Mammographic Mass Detection and Classification

  • Daniela Giordano
  • Isaak Kavasidis
  • Concetto Spampinato
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 166)

Abstract

This paper presents an automated knowledge-based vision system for mass detection and classification in X-Ray mammograms. The system developed herein is based on several processing steps, which aim first at identifying the various regions of the mammogram such as breast, markers, artifacts and background area and then to analyze the identified areas by applying a contrast improvement method for highlighting the pixels of the candidate masses. The detection of such candidate masses is then done by applying locally a 2D Haar Wavelet transform, whereas the mass classification (in benign and malignant ones) is performed by means of a support vector machine whose features are the spatial moments extracted from the identified masses. The system was tested on the public database MIAS achieving very promising results in terms both of accuracy and of sensitivity.

Keywords

Biomedical Image Processing X-Ray Local Image Enhancement Support Vector Machines 

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References

  1. 1.
    Egan, R.: Breast Imaging: Diagnosis and Morphology of Breast Diseases. Saunders Co Ltd. (1988)Google Scholar
  2. 2.
    Giordano, D., Spampinato, C., Scarciofalo, G., Leonardi, R.: EMROI extraction and classification by adaptive thresholding and DoG filtering for automated skeletal bone age analysis. In: Proc. of the 29th EMBC Conference, pp. 6551–6556 (2007)Google Scholar
  3. 3.
    Giordano, D., Spampinato, C., Scarciofalo, G., Leonardi, R.: An automatic system for skeletal bone age measurement by robust processing of carpal and epiphysial/metaphysial bones. IEEE Transactions on Instrumentation and Measurement 59(10), 2539–2553 (2010)CrossRefGoogle Scholar
  4. 4.
    Hadhou, M., Amin, M., Dabbour, W.: Detection of breast cancer tumor algorithm using mathematical morphology and wavelet analysis. In: Proc. of GVIP 2005, pp. 208–213 (2005)Google Scholar
  5. 5.
    Kecman, V.: Learning and Soft Computing, Support Vector Machines, Neural Networks and Fuzzy Logic Models. MIT Press, Cambridge (2001)zbMATHGoogle Scholar
  6. 6.
    Kom, G., Tiedeu, A., Kom, M.: Automated detection of masses in mammograms by local adaptive thresholding. Comput. Biol. Med. 37, 37–48 (2007)CrossRefGoogle Scholar
  7. 7.
    Oliver, A., Freixenet, J., Marti, J., Perez, E., Pont, J., Denton, E.R., Zwiggelaar, R.: A review of automatic mass detection and segmentation in mammographic images. Med. Image Anal. 14, 87–110 (2010)CrossRefGoogle Scholar
  8. 8.
    Raviraj, P., Sanavullah, M.: The modified 2D Haar wavelet transformation in image compression. Middle-East Journ. of Scient. Research 2 (2007)Google Scholar
  9. 9.
    Rejani, Y.I.A., Selvi, S.T.: Early detection of breast cancer using SVM classifier technique. CoRR, abs/0912.2314 (2009)Google Scholar
  10. 10.
    Rojas Dominguez, A., Nandi, A.K.: Detection of masses in mammograms via statistically based enhancement, multilevel-thresholding segmentation, and region selection. Comput. Med. Imaging Graph 32, 304–315 (2008)CrossRefGoogle Scholar
  11. 11.
    Sampat, M., Markey, M., Bovik, A.: Computer-aided detection and diagnosys in mammography. In: Handbook of Image and Video Processing, 2nd edn., pp. 1195–1217 (2005)Google Scholar
  12. 12.
    Shi, J., Sahiner, B., Chan, H.P., Ge, J., Hadjiiski, L., Helvie, M.A., Nees, A., Wu, Y.T., Wei, J., Zhou, C., Zhang, Y., Cui, J.: Characterization of mammographic masses based on level set segmentation with new image features and patient information. Med. Phys. 35, 280–290 (2008)CrossRefGoogle Scholar
  13. 13.
    Suckling, J., Parker, D., Dance, S., Astely, I., Hutt, I., Boggis, C.: The mammographic images analysis society digital mammogram database. Exerpta Medical International Congress Series, pp. 375–378 (1994)Google Scholar
  14. 14.
    Suliga, M., Deklerck, R., Nyssen, E.: Markov random field-based clustering applied to the segmentation of masses in digital mammograms. Comput. Med. Imaging Graph 32, 502–512 (2008)CrossRefGoogle Scholar
  15. 15.
    Timp, S., Karssemeijer, N.: A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography. Med. Phys. 31, 958–971 (2004)CrossRefGoogle Scholar
  16. 16.
    Wei, J., Sahiner, B., Hadjiiski, L.M., Chan, H.P., Petrick, N., Helvie, M.A., Roubidoux, M.A., Ge, J., Zhou, C.: Computer-aided detection of breast masses on full field digital mammograms. Med. Phys. 32, 2827–2838 (2005)CrossRefGoogle Scholar
  17. 17.
    Zhang, L., Sankar, R., Qian, W.: Advances in micro-calcification clusters detection in mammography. Comput. Biol. Med. 32, 515–528 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Daniela Giordano
    • 1
  • Isaak Kavasidis
    • 1
  • Concetto Spampinato
    • 1
  1. 1.Department of Electrical, Electronics and Informatics EngineeringUniversity of CataniaCataniaItaly

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