Toward Expert Knowledge Representation for Automatic Breast Cancer Detection

  • Marina Velikova
  • Maurice Samulski
  • Nico Karssemeijer
  • Peter Lucas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5253)

Abstract

In reading mammograms, radiologists judge for the presence of a lesion by comparing at least two breast projections (views) as a lesion is to be observed in both of them. Most computer-aided detection (CAD) systems, on the other hand, treat single views independently and thus they fail to account for the interaction between the breast views. Following the radiologist’s practice, in this paper, we develop a Bayesian network framework for automatic multi-view mammographic analysis based on causal independence models and the regions detected as suspicious by a single-view CAD system. We have implemented two versions of the framework based on different definitions of multi-view correspondences. The proposed approach is evaluated and compared against the single-view CAD system in an experimental study with real-life data. The results show that using expert knowledge helps to increase the cancer detection rate at a patient level.

Keywords

Bayesian network causal independence model mammography multi-view breast cancer detection 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Good, W., Zheng, B., Chang, Y., Wang, X., Maitz, G., Gur, D.: Multi-image cad employing features derived from ipsilateral mammographic views. Proceedings of SPIE, Medical Imaging 3661, 474–485 (1999)Google Scholar
  2. 2.
    van Engeland, S., Timp, S., Karssemeijer, N.: Finding corresponding regions of interest in mediolateral oblique and craniocaudal mammographic views. Medical Physics 33(9), 3203–3212 (2006)CrossRefGoogle Scholar
  3. 3.
    van Engeland, S., Karssemeijer, N.: Combining two mammographic projections in a computer aided mass detection method. Medical Physics 34(3), 898–905 (2007)CrossRefGoogle Scholar
  4. 4.
    Paquerault, S., Petrick, N., Chan, H., Sahiner, B., Helvie, M.A.: Improvement of computerized mass detection on mammograms: Fusion of two-view information. Medical Physics 29(2), 238–247 (2002)CrossRefGoogle Scholar
  5. 5.
    Velikova, M., de Carvalho Ferreira, N., Lucas, P.: Bayesian network decomposition for modeling breast cancer detection. In: Bellazzi, R., Abu-Hanna, A., Hunter, J. (eds.) AIME 2007. LNCS (LNAI), vol. 4594. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Jensen, F.V., Nielsen, T.D.: Bayesian Networks and Decision Graphs. Springer, Heidelberg (2007)MATHGoogle Scholar
  7. 7.
    Heckerman, D., Breese, J.S.: Causal independence for probability assessment and inference using Bayesian networks. IEEE Transactions on Systems, Man and Cybernetics, Part A 26(6), 826–831 (1996)CrossRefGoogle Scholar
  8. 8.
    Lucas, P.J.F.: Bayesian network modelling through qualitative pattern. Artificial Intelligence 163, 233–263 (2005)MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Diez, F.: Parameter adjustment in Bayes networks: The generalized noisy or-gate. In: Proceedings of the Ninth Conference on UAI, San Francisco, CA (1993)Google Scholar
  10. 10.
    Murphy, K.: Bayesian Network Toolbox (BNT), http://www.cs.ubc.ca/~murphyk/Software/BNT/bnt.html
  11. 11.
    Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Stat. Soc., Series B 39(1), 1–38 (1977)MATHMathSciNetGoogle Scholar
  12. 12.
    Hanley, J.A., McNeil, B.J.: The meaning and use of the area under a Receiver Operating Characteristic (ROC) curve. Radiology 143, 29–36 (1982)Google Scholar
  13. 13.
    Metz, C.E.: Some practical issues of experimental design and data analysis in radiological ROC studies. Investigative Radiology 24, 234–245 (1988)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Marina Velikova
    • 1
  • Maurice Samulski
    • 1
  • Nico Karssemeijer
    • 1
  • Peter Lucas
    • 2
  1. 1.Department of RadiologyRadboud University Nijmegen Medical CentreNijmegenThe Netherlands
  2. 2.Institute for Computing and Information SciencesRadboud University NijmegenNijmegenThe Netherlands

Personalised recommendations