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Computer Aided Classification of Mammographic Tissue Using Shapelets and Support Vector Machines

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Artificial Intelligence: Methods and Applications (SETN 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8445))

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Abstract

In this paper a robust regions-of-suspicion (ROS) diagnosis system on mammograms, recognizing all types of abnormalities is presented and evaluated. A new type of descriptors, based on Shapelet decomposition, derive the source images that generate the observed ROS in mammograms. The Shapelet decomposition coefficients can be used efficiently to detect ROS areas using Support-Vector-Machines (SVMs) with radial basis function kernels. Extensive experiments using the Mammographic Image Analysis Society (MIAS) database have shown high recognition accuracy above 86% for all kinds of breast abnormalities that exceeds the performance of similar decomposition methods based on Zernike moments presented in the literature by more than 8%.

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Author information

Authors and Affiliations

  1. Wired Communications Lab., Electrical and Computer Engineering Department, University of Patras, Greece

    George Apostolopoulos, Athanasios Koutras, Ioanna Christoyianni & Evaggelos Dermatas

  2. Informatics and Mass Media Department, Technical Educational Institute of Western Greece, Greece

    Athanasios Koutras

Authors
  1. George Apostolopoulos
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  2. Athanasios Koutras
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  3. Ioanna Christoyianni
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  4. Evaggelos Dermatas
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Ioannina, GR 45110, Ioannina, Greece

    Aristidis Likas

  2. Department of Computer Science, University of Ioannina, P.O. Box 1186, 45110, Ioannina, Greece

    Konstantinos Blekas

  3. Hellenic Open University, GR 26335, Peribola, Patras, Greece

    Dimitris Kalles

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Apostolopoulos, G., Koutras, A., Christoyianni, I., Dermatas, E. (2014). Computer Aided Classification of Mammographic Tissue Using Shapelets and Support Vector Machines. In: Likas, A., Blekas, K., Kalles, D. (eds) Artificial Intelligence: Methods and Applications. SETN 2014. Lecture Notes in Computer Science(), vol 8445. Springer, Cham. https://doi.org/10.1007/978-3-319-07064-3_44

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  • DOI: https://doi.org/10.1007/978-3-319-07064-3_44

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07063-6

  • Online ISBN: 978-3-319-07064-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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