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Breast Tissue Classification in Mammograms Using ICA Mixture Models

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Artificial Neural Networks — ICANN 2001 (ICANN 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2130))

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Abstract

In this paper we present a novel method for recognizing all kinds of abnormalities in digital mammograms using Independent Component Analysis mixture models and two sets of statistical features based on texture analysis.Our approach is concentrated on finding the ICA mixture model parameters that describe in an exclusive and effective way the abnormal and the normal tissue, and with the aid of a supervised probabilistic classifier we are able to successfully recognize suspicious regions in mammograms. Extensive experiments using the MIAS database have shown great accuracy of 98.33% in classifying an unknown regions of suspicion as abnormal and 62.71% as healthy tissue.

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Author information

Authors and Affiliations

  1. WCL, Electrical & Computer Engineering Dept., University of Patras, 26100, Patras, Hellas

    Ioanna Christoyianni, Athanasios Koutras, Evangelos Dermatas & George Kokkinakis

Authors
  1. Ioanna Christoyianni
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  2. Athanasios Koutras
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  3. Evangelos Dermatas
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  4. George Kokkinakis
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Editor information

Editors and Affiliations

  1. Dept. of Mecidal Cybernetics and Artificial Intelligence, University of Vienna, Freyung 6/2, 1010, Vienna, Austria

    Georg Dorffner

  2. Institute for Computer Aided Automation Pattern Recognition and Image Processing Group, Technical University of Vienna, Favoritenstr. 9/1832, 1040, Vienna, Austria

    Horst Bischof

  3. Institut für Statistik, Wirtschaftsuniversität Wien, Augasse 2-6, 1090, Wien, Austria

    Kurt Hornik

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© 2001 Springer-Verlag Berlin Heidelberg

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Christoyianni, I., Koutras, A., Dermatas, E., Kokkinakis, G. (2001). Breast Tissue Classification in Mammograms Using ICA Mixture Models. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_78

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  • DOI: https://doi.org/10.1007/3-540-44668-0_78

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42486-4

  • Online ISBN: 978-3-540-44668-2

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