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.
Chapter PDF
Similar content being viewed by others
Keywords
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
Devijver, P.A., Kittler, J.: Pattern Recognition: A Statistical Approach. Prentice-Hall, England (1982)
Donoho, D.L., JohnStone, I.M.: Ideal spatial adaptation by wavelet shrinkage. Biometrika 81, 425–455 (1994)
Ferreira, C.B.R., Borges, D.L.: Analysis of mammogram classification using a wavelet transform decomposition. Pattern Recognition Letters 24, 973–982 (2003)
http://www.wiau.man.ac.uk/services/MIAS (Mammographic Image Analysis Society)
Jain, R., Kasturi, R., Schunck, B.: Machine Vision. McGraw Hill, USA (1995)
Mallat, S.G.: A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 11, 674–693 (1989)
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)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ferreira, C.B.R., Borges, D.L. (2005). An Evaluation of Wavelet Features Subsets for Mammogram Classification. In: Sanfeliu, A., Cortés, M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11578079_65
Download citation
DOI: https://doi.org/10.1007/11578079_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29850-2
Online ISBN: 978-3-540-32242-9
eBook Packages: Computer ScienceComputer Science (R0)