A Boosting-Based Approach to Refine the Segmentation of Masses in Mammography

  • Mario Molinara
  • Claudio Marrocco
  • Francesco Tortorella
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8157)


In this paper we present an algorithm for finding an accurate estimate of the contour of masses in mammograms. We assume that a rough estimate of the region containing the mass is known: in particular it is available the location of an area inside the mass (core) and a closed curve beyond which the mass does not extend. The proposed method employs a boosting-based classifier trained on the core and on a background region beyond the external contour, so that it provides an accurate estimate of the mass contour by classifying unlabeled pixels between the core and the external contour. The proposed approach is useful not only for automatic localization of mass contour, but also as a powerful tool during annotation of mammograms, given that an user provides interactively an estimate for the core and the external contour of the mass. The approach has been verified on a set of mammograms showing very encouraging results.


Mammography Mass Segmentation Boosting 


  1. 1.
    Cancer Facts & Figures 2012. American Cancer Society, Atlanta (2012)Google Scholar
  2. 2.
    Avidan, S.: Spatialboost: Adding spatial reasoning to adaboost. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 386–396. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Cheng, H., Cai, X., Chen, X., Hu, L., Lou, X.: Computer-aided detection and classification of microcalcification in mammograms: a survey. Pattern Recognition 36, 2967–2991 (2003)CrossRefzbMATHGoogle Scholar
  4. 4.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, pp. 886–893 (2005)Google Scholar
  5. 5.
    Giger, M.: Computer-aided diagnosis of breast lesions in medical images. Computing in Science and Engg. 2, 39–45 (2000)CrossRefGoogle Scholar
  6. 6.
    Heath, M., Bowyer, K., Kopans, D., Moore, R., Kegelmeyer, W.P.: The digital database for screening mammography. In: Yaffe, M. (ed.) Proc. 5th Int. Workshop on Digital Mammography, pp. 212–218 (2001)Google Scholar
  7. 7.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  8. 8.
    Marrocco, C., Molinara, M., Delia, C., Tortorella, F.: A computer-aided detection system for clustered microcalcifications. Artificial Intelligence in Medicine 50(1), 23–32 (2010)CrossRefGoogle Scholar
  9. 9.
    Nishikawa, R.: Current status and future directions of computer-aided diagnosis in mammography. Computerized Medical Imaging and Graphics 31, 1357–1376 (2007)CrossRefGoogle Scholar
  10. 10.
    Rangayyan, R., Ayres, F.J., Desautels, J.: A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs. Journal of the Franklin Institute 344(3-4), 312–348 (2007)CrossRefzbMATHGoogle Scholar
  11. 11.
    Tang, J., Rangayyan, R., Xu, J., El Naqa, I., Yang, Y.: Computer-aided detection and diagnosis of breast cancer with mammography: recent advances. Trans. Info. Tech. Biomed. 13, 236–251 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mario Molinara
    • 1
  • Claudio Marrocco
    • 1
  • Francesco Tortorella
    • 1
  1. 1.Dept. of Electrical and Information EngineeringUniversità degli Studi di Cassino e del Lazio MeridionaleCassinoItaly

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