Robustness of a CAD System on Digitized Mammograms

  • Antonio García-Manso
  • Carlos J. García-Orellana
  • Ramón Gallardo-Caballero
  • Nico Lanconelli
  • Horacio González-Velasco
  • Miguel Macías-Macías
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7477)


In this paper we study the robustness of our CAD system, since this is one of the main factors that determine its quality. A CAD system must guarantee consistent performance over time and in various clinical situations. Our CAD system is based on the extraction of features from the mammographic image by means of Independent Component Analysis, and machine learning classifiers, such as Neural Networks and Support Vector Machine. To measure the robustness of our CAD system we have used the digitized mammograms of the USF’s DDSM database, because this database was built by digitizing mammograms from four different institutions (four different scanner) during more than 10 years. Thus, we can use the mammograms digitized with one scanner to train the system and the remaining to evaluate the performance, what gives us a measure of the robustness of our CAD system.




  1. 1.
    Serio, G.V., Novello, A.C.: The advisability of the adoption of a law that would expand the definition of mammography screening to include the review of x-ray examinations by use of a computer aided detection device. Technical report, The Superintendent of Insurance in consulation with the Commissioner of Health, USA (2003)Google Scholar
  2. 2.
    Heath, M., Bowyer, K., Kopans, D., Moore, R., Kegelmeyer, P.: The digital database for screening mammography. In: Proceedings of the 5th International Workshop on Digital Mammography, pp. 212–218 (2000)Google Scholar
  3. 3.
    Horsch, A., Hapfelmeier, A., Elter, M.: Needs assessment for next generation computer-aided mammography reference image databases and evaluation studies. International Journal of Computer Assisted Radiology and Surgery, 1–19 (2011)Google Scholar
  4. 4.
    Hyvärinen, A., Hurri, J., Hoyer, P.O.: Natural Image Statistics. A Probablisctic Approach to Early Computational Vision. Springer (2009)Google Scholar
  5. 5.
    Anjali, P., Ajay, S.: A review on natural image denoising using independent component analysis (ICA) technique. Advances in Computational Research 2(1), 06–14 (2010)Google Scholar
  6. 6.
    Campanini, R., Dongiovanni, D., Iampieri, E., Lanconelli, N., Masotti, M., Palermo, G., Riccardi, A., Roffilli, M.: A novel featureless approach to mass detection in digital mammograms based on support vector machines. Physics in Medicine and Biology 49, 961–975 (2004)CrossRefGoogle Scholar
  7. 7.
    Angelini, E., Campanini, R., Iampieri, E., Lanconelli, N., Masotti, M., Roffilli, M.: Testing the performances of different image representations for mass classification in digital mammograms. International Journal of Modern Physics C [Computational Physics and Physical Computation] 17(1), 113–131 (2006)zbMATHCrossRefGoogle Scholar
  8. 8.
    Hong, B.-W., Brady, J.M.: A Topographic Representation for Mammogram Segmentation. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2879, pp. 730–737. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  9. 9.
    Bradski, G., Kaehler, A.: Learning OpenCV: Computer Vision with the OpenCV Library. O’Reilly Media (2008)Google Scholar
  10. 10.
    Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Adaptive and Learning Systems for Signal Processing, Communications, and Control. John Wiley & Sons (2001)Google Scholar
  11. 11.
    Ripley, B.: FastICA Algorithms to perform ICA and Projection Pursuit (February 2009),
  12. 12.
    Jolliffe, I.T.: Principal Component Analysis, 2nd edn. Series in Statistics. Springer (2002)Google Scholar
  13. 13.
    Gonzalez, R.C., Woods, R.E.: Digital image processing, 3rd edn. Prentice-Hall, Upper Saddle River (2008)Google Scholar
  14. 14.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer (2006)Google Scholar
  15. 15.
    Vapnik, V.N.: The Nature of Statistical Learning Theory, 2nd edn. Statistics for Engineering and Information Science. Springer (2000)Google Scholar
  16. 16.
    Riedmiller, H., Braun, H.: A direct adaptive method for faster backpropagation learning. The RPROP algorithm. In: IEEE International Conference on Neural Networks, pp. 586–591 (1993)Google Scholar
  17. 17.
    Zell, A., Mache, N., Huebner, R., Mamier, G., Vogt, M., Schmalzl, M., Herrmann, K.: SNNS (Stuttgart Neural Network Simulator). In: Neural Network Simulation Environments, pp. 165–186 (1994)Google Scholar
  18. 18.
    Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2(3), 27:1–27:27 (2011)Google Scholar
  19. 19.
    Lausser, L., Kestler, H.A.: Robustness Analysis of Eleven Linear Classifiers in Extremely High–Dimensional Feature Spaces. In: Schwenker, F., El Gayar, N. (eds.) ANNPR 2010. LNCS, vol. 5998, pp. 72–83. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  20. 20.
    Bowyer, K.W.: Digital Image Database with Gold Standard and Performance Metrics for Mammographic Image Analysis Research. Technical report, U.S. Army Medical Research and Materiel Command Fort Detrick, Maryland 21702-5012 (August 1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Antonio García-Manso
    • 1
  • Carlos J. García-Orellana
    • 1
  • Ramón Gallardo-Caballero
    • 1
  • Nico Lanconelli
    • 2
  • Horacio González-Velasco
    • 1
  • Miguel Macías-Macías
    • 1
  1. 1.Pattern Classification and Image Analysis Group (CAPI)University of ExtremaduraBadajozSpain
  2. 2.Medical Imaging Group, Physics Dpt.Bologna UniversityBolognaItaly

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