An Evaluation of Wavelet Features Subsets for Mammogram Classification

  • Cristiane Bastos Rocha Ferreira
  • Díbio Leandro Borges
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)

Abstract

This paper is about an evaluation for a feature selection strategy for mammogram classification. An earlier solution to this problem is revisited, which constructed a supervised classifier for two problems in mammogram classification: tumor nature, and tumor geometric type. The approach works by transforming the data of the images in a wavelet basis and by using a minimum subset of representative features of these textures based in a specific threshold (λ T ). In this paper different wavelet bases, variation of the selection strategy for the coefficients, and different metrics are all evaluated with known labelled images. This is a suitable solution worth further exploration. For the experiments we have used samples of images labeled by physicians. Results shown are promising, and we describe possible lines for future directions.

Keywords

Mahalanobis Distance Wavelet Basis Gabor Wavelet Representative Feature Tumor Nature 
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.

References

  1. 1.
    Devijver, P.A., Kittler, J.: Pattern Recognition: A Statistical Approach. Prentice-Hall, England (1982)MATHGoogle Scholar
  2. 2.
    Donoho, D.L., JohnStone, I.M.: Ideal spatial adaptation by wavelet shrinkage. Biometrika 81, 425–455 (1994)MATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Ferreira, C.B.R., Borges, D.L.: Analysis of mammogram classification using a wavelet transform decomposition. Pattern Recognition Letters 24, 973–982 (2003)CrossRefGoogle Scholar
  4. 4.
    http://www.wiau.man.ac.uk/services/MIAS (Mammographic Image Analysis Society)
  5. 5.
    Jain, R., Kasturi, R., Schunck, B.: Machine Vision. McGraw Hill, USA (1995)Google Scholar
  6. 6.
    Mallat, S.G.: A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 11, 674–693 (1989)MATHCrossRefGoogle Scholar
  7. 7.
    Qi, H., Kuruganti, P., Liu, Z.: Early detection of breast cancer using thermal texture maps. In: IEEE Symposium on Biomedical Imaging: Macro to Nano (2002)Google Scholar
  8. 8.
    Rangayyan, R.M., Ferrari, R.J., Desautels, J.E.L., Frère, A.F.: Directional analysis of images with Gabor wavelets. In: Proceedings of XIII Brazilian Symposium on Computer Graphics and Image Processing, SIBGRAPI, pp. 170–177 (2000)Google Scholar
  9. 9.
    Woods, K.S.: Automated image analysis techniques for digital mammography, Ph. D thesis, Dept C. Science and Engineering, University of South Florida, FL, USA (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Cristiane Bastos Rocha Ferreira
    • 1
  • Díbio Leandro Borges
    • 2
  1. 1.Instituto de InformáticaUniversidade Federal de GoiásGoiâniaBrazil
  2. 2.BIOSOLOGoiâniaBrazil

Personalised recommendations