Abnormal masses in mammograms: Detection using scale-orientation signatures

  • Reyer Zwiggelaar
  • Christopher J. Taylor
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1496)


We describe a method for labelling image structure based on scale-orientation signatures. These signatures provide a rich and stable description of local structure and can be used as a basis for robust pixel classification. We use a multi-scale directional recursive median filtering technique to obtain local scale-orientation signatures. Our results show that the new method of representation is robust to the presence of both random and structural noise. We demonstrate application to synthetic images containing lines and blob-like features and to mammograms containing abnormal masses. Quantitative results are presented, using both linear and non-linear classification methods.


Principal Component Analysis Model Background Texture Linear Classification Temporal Subtraction False Positive Fraction 
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.


  1. 1.
    J.A. Bangham, T.G. Campbell, and R.V. Aldridge. Multiscale median and morphological filters for 2d pattern recognition. Signal Processing, 38:387–415, 1994.CrossRefGoogle Scholar
  2. 2.
    R. Harvey, A. Bosson, and J.A. Bangham. The robustness of some scale-spaces. In Proceedings of the 8 th British Machine Vision Conference, pages 11–20, Colchester, UK, 1997.Google Scholar
  3. 3.
    I.T. Jolliffe. Principal Component Analysis. Springer Verlag, 1986.Google Scholar
  4. 4.
    S.N. Kalitzin, B.M. ter Haar Romeny, and MA. Viergever. Invertible orientation bundles on 2d scalar images. In Lecture Notes in Computer Science, 1252:77–88, 1997.CrossRefGoogle Scholar
  5. 5.
    C.E. Metz. Evaluation of digital mammography by roc analysis. Excerpta Medica, 1119:61–68, 1996.Google Scholar
  6. 6.
    University of Stuttgart. Suttgart Neural Network Simulator. User Manual, Version 4.1, 1995.Google Scholar
  7. 7.
    N. Petrick, H.-P. Chan, B. Sahiner, M.A. Helvie, M.M. Goodsitt, and D.D. Adler. Computer-aided breast mass detection: false positive reduction using breast tissue composition. Excerpta Medica, 1119:373–378, 1996.Google Scholar
  8. 8.
    P. Soille, E.J. Breen, and R. Jones. Recursive implementation of erosions and dilations along discrete lines at arbitrary angles. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(5):562–567, 1996.CrossRefGoogle Scholar
  9. 9.
    W.K. Zouras, M.L. Giger, P. Lu, D.E. Wolverton, C.J. Vyborny, and K. Doi. Investigation of a temporal subtraction scheme for computerized detection of breast masses in mammograms. Excerpta Medica, 1119:411–415, 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Reyer Zwiggelaar
    • 1
  • Christopher J. Taylor
    • 2
  1. 1.Division of Computer ScienceUniversity of PortsmouthPortsmouthUK
  2. 2.Wolfson Image Analysis UnitUniversity of ManchesterManchesterUK

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