Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 213794, 10 pages
Research Article

Study of the Effect of Breast Tissue Density on Detection of Masses in Mammograms

Pattern Classification and Image Analysis Group, University of Extremadura, Avenida de Elvas s/n, Badajoz, 06006 Extremadura, Spain

Received 4 October 2012; Accepted 21 February 2013

Academic Editor: Angel García-Crespo

Copyright © 2013 A. García-Manso et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. O. Peart, Mammography and Breast Imaging. Just the Facts, McGraw-Hill, New York, NY, USA, 1st edition, 2005.
  2. D. Kopans, Breast Imaging, Lippincott Williams & Wilkins, Baltimore, Md, USA, 2007.
  3. R. E. Bird, T. W. Wallace, and B. C. Yankaskas, “Analysis of cancers missed at screening mammography,” Radiology, vol. 184, no. 3, pp. 613–617, 1992. View at Google Scholar · View at Scopus
  4. T. M. Kolb, J. Lichy, and J. H. Newhouse, “Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations,” Radiology, vol. 225, no. 1, pp. 165–175, 2002. View at Google Scholar · View at Scopus
  5. M. T. Mandelson, N. Oestreicher, P. L. Porter et al., “Breast density as a predictor of mammographic detection: comparison of interval- and screen-detected cancers,” Journal of the National Cancer Institute, vol. 92, no. 13, pp. 1081–1087, 2000. View at Google Scholar · View at Scopus
  6. R. F. Brem, J. W. Hoffmeister, J. A. Rapelyea et al., “Impact of breast density on computer-aided detection for breast cancer,” American Journal of Roentgenology, vol. 184, no. 2, pp. 439–444, 2005. View at Google Scholar · View at Scopus
  7. J. Freixenet, A. Oliver, R. Martí et al., “Eigendetection of masses considering false positive reduction and breast density information,” Medical Physics, vol. 35, no. 5, pp. 1840–1853, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. A. Oliver, J. Freixenet, J. Martí et al., “A review of automatic mass detection and segmentation in mammographic images,” Medical Image Analysis, vol. 14, no. 2, pp. 87–110, 2010. View at Publisher · View at Google Scholar · View at Scopus
  9. A. Horsch, A. Hapfelmeier, and M. Elter, “Needs assessment for next generation computer-aided mammography reference image databases and evaluation studies,” International Journal of Computer Assisted Radiology and Surgery, vol. 6, no. 6, pp. 749–767, 2011. View at Google Scholar
  10. M. Heath, K. Bowyer, D. Kopans, R. Moore, and P. Kegelmeyer, “The digital database for screening mammography,” in proceedings of the 5th International Workshop on Digital Mammography (IWDM '01), M. J. Yae, Ed., pp. 212–218, 2001.
  11. R. Campanini, D. Dongiovanni, E. Iampieri et al., “A novel featureless approach to mass detection in digital mammograms based on support vector machines,” Physics in Medicine and Biology, vol. 49, no. 6, pp. 961–975, 2004. View at Publisher · View at Google Scholar · View at Scopus
  12. E. Angelini, R. Campanini, E. Iampieri, N. Lanconelli, M. Masotti, and M. Roffilli, “Testing the performances of different image representations for mass classification in digital mammograms,” International Journal of Modern Physics C, vol. 17, no. 1, pp. 113–131, 2006. View at Publisher · View at Google Scholar · View at Scopus
  13. B. W. Hong and M. Brady, “A topographic representation for mammogram segmentation,” Lecture Notes in Computer Science, vol. 2879, pp. 730–737, 2003. View at Google Scholar
  14. G. Bradski and A. Kaehler, Learning OpenCV: Computer Vision With the OpenCV Library, O'Reilly Media, 1st edition, 2008.
  15. A. Hyvärinen, J. Karhunen, and E. Oja, Independent Component Analysis. Adaptive and Learning Systems for Signal Processing, Communications, and Control, John Wiley & Sons, New York, NY, USA, 2001.
  16. M. Rolli, Advanced machine learning techniques for digital mammography [Ph.D. thesis], Department of Computer Science, University of Bologna, Bologna, Italy, 2006.
  17. C. M. Bishop, Pattern Recognition and Machine Learning, Springer, New York, NY, USA, 2006.
  18. S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall, New York, NY, USA, 1999.
  19. V. N. Vapnik, The Nature of Statistical Learning Theory, Statistics for Engineering and Information Science, Springer, New York, NY, USA, 2nd edition, 2000.
  20. R. M. Rangayyan, F. J. Ayres, and J. E. Leo Desautels, “A review of computer-aided diagnosis of breast cancer: toward the detection of subtle signs,” Journal of the Franklin Institute, vol. 344, no. 3-4, pp. 312–348, 2007. View at Publisher · View at Google Scholar · View at Scopus
  21. I. Gonzalez-Carrasco, A. Garcia-Crespo, B. Ruiz-Mezcua, and J. L. Lopez-Cuadrado, “An optimization methodology for machine learning strategies andregression problems in ballistic impact scenarios,” Applied Intelligence, vol. 36, no. 2, pp. 424–441, 2012. View at Publisher · View at Google Scholar · View at Scopus
  22. M. Riedmiller and H. Braun, “Direct adaptive method for faster backpropagation learning: the RPROP algorithm,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 586–591, April 1993. View at Scopus
  23. A. Zell, N. Mache, R. Huebner et al., “SNNS (Stuttgart Neural Network Simulator),” Neural Network Simulation Environments, vol. 254, pp. 165–186, 1994. View at Google Scholar
  24. C. C. Chang and C. J. Lin, “LIBSVM: a Library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, article 27, 2011. View at Publisher · View at Google Scholar · View at Scopus
  25. A. Hyvarinen, “Fast and robust fixed-point algorithms for independent component analysis,” IEEE Transactions on Neural Networks, vol. 10, no. 3, pp. 626–634, 1999. View at Publisher · View at Google Scholar · View at Scopus
  26. B. Ripley, “FastICA algorithms to perform ICA and projection pursuit,” 2009,
  27. T. Fawcett, “An introduction to ROC analysis,” Pattern Recognition Letters, vol. 27, no. 8, pp. 861–874, 2006. View at Publisher · View at Google Scholar · View at Scopus