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Exploitation of Correspondence Between CC and MLO Views in Computer Aided Mass Detection

  • Saskia van Engeland
  • Nico Karssemeijer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4046)

Abstract

In this paper we investigate the effect of reclassification of CAD findings using correspondences in MLO and CC views, with the aim of reducing false positives and inconsistencies. We use a method to link regions identified as suspicious in both projections and add a two-view classifier to an existing CAD scheme. The input of this two-view classifier was a feature vector containing the likelihood of malignancy of the region, the likelihood of malignancy of the corresponding region in the other view, and a number of features that describe the resemblance between the both regions. Using FROC analysis we show that detection results improve when using two-view information.

Keywords

Linear Discriminant Analysis Single View Abnormal Case Forward Feature Selection Correspondence Score 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Nishikawa, R.M., Edwards, A., Schmidt, R.A., Papaioannou, J., Linver, M.N.: Can radiologists recognize that a computer has identified cancers that they have overlooked? In: SPIE Medical Imaging, vol. 6146 (2006)Google Scholar
  2. 2.
    Zheng, B., Chough, D., Ronald, P., Cohen, C., Hakim, C.M., Abrams, G., Ganott, M.A., Wallace, L., Shah, R., Sumkin, J.H., Gur, D.: Actual versus intended use of CAD systems in the clinical environment. In: SPIE Medical Imaging, vol. 6146 (2006)Google Scholar
  3. 3.
    Karssemeijer, N., te Brake, G.M.: Detection of stellate distortions in mammograms. IEEE Trans. Med. Imag. 15, 611–619 (1996)CrossRefGoogle Scholar
  4. 4.
    te Brake, G.M., Karssemeijer, N.: Segmentation of suspicious densities in digital mammograms. Medical Physics 28, 259–266 (2001)CrossRefGoogle Scholar
  5. 5.
    Kita, Y., Tohno, E., Highnam, R., Brady, M.: A CAD system for the 3D location of lesions in mammograms. Med. Image Anal. 6(3), 267–273 (2002)CrossRefGoogle Scholar
  6. 6.
    Chang, Y.H., Good, W.F., Sumkin, J.H., Zheng, B., Gur, D.: Computerized localization of breast lesions from two views. an experimental comparison of two methods. Invest Radiol. 34(9), 585–588 (1999)CrossRefGoogle Scholar
  7. 7.
    van Engeland, S., Karssemeijer, N.: Matching breast lesions in multiple mammographic views. In: Agha, G.A., De Cindio, F., Rozenberg, G. (eds.) APN 2001. LNCS, vol. 2208, pp. 1172–1173. Springer, Heidelberg (2001)Google Scholar
  8. 8.
    Paquerault, S., Petrick, N., Chan, H.P., Sahiner, B., Helvie, M.A.: Improvement of computerized mass detection on mammograms: fusion of two-view information. Med. Phys. 29(2), 238–247 (2002)CrossRefGoogle Scholar
  9. 9.
    Karssemeijer, N.: Automated classification of parenchymal patterns in mammograms. Phys. Med. Biol. 43, 365–378 (1998)CrossRefGoogle Scholar
  10. 10.
    Good, W.F., Zheng, B., Chang, Y.-H., Wang, X.H., Maitz, G., Gur, D.: Multi-image cad employing features derived from ipsilateral mammographic views. In: SPIE 1999 Image processing, vol. 3661, pp. 474–485 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Saskia van Engeland
    • 1
  • Nico Karssemeijer
    • 1
  1. 1.Department of RadiologyRadboud University Nijmegen Medical CentreThe Netherlands

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