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International Symposium on Medical Data Analysis

ISMDA 2002: Medical Data Analysis pp 20–31Cite as

Microcalcification Detection Using a Kernel Bayes Classifier

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2526))

Abstract

Mammography associated with clinical breast examination is the only effective method for mass breast screening. Microcalcifications are one of the primary signs for early detection of breast cancer. In this paper we propose a new kernel method for classification of dificult to diagnose regions in mammographic images. It consists of a novel class of Markov Random Fields, using techniques developed within the context of statistical mechanics. This method is used for the classification of positive Region of Interest (ROI’s) containing clustered microcalcifications and negative ROI’s containing normal tissue. We benchmarked the new proposed method with a nearest neighbor classifier and with an artificial neural network, widely used in literature for computer-aided diagnosis. We obtained the best performance using the novel approach.

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Author information

Authors and Affiliations

  1. CISB- Physics Department, University of Rome ‘La Sapienza’, Piazza A. Moro 3, 00161, Rome, Italy

    B. Caputo

  2. Smith-Kettlewell Eye Research Institute, 2318 Fillmore Street, 94115, San Francisco, California, USA

    B. Caputo, E. La Torre & G. E. Gigante

Authors
  1. B. Caputo
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  2. E. La Torre
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  3. G. E. Gigante
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Editor information

Editors and Affiliations

  1. Dept. of Human Physiology and Pharmacology, University of Rome La Sapienza, P. le A. Moro, 5, 00185, Rome, Italy

    Alfredo Colosimo  & Paolo Sirabella  & 

  2. TCE Laboratory, Istituto Superiore di Sanità, V. le Regina Elena, 299, 00161, Rome, Italy

    Alessandro Giuliani

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© 2002 Springer-Verlag Berlin Heidelberg

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Caputo, B., La Torre, E., Gigante, G.E. (2002). Microcalcification Detection Using a Kernel Bayes Classifier. In: Colosimo, A., Sirabella, P., Giuliani, A. (eds) Medical Data Analysis. ISMDA 2002. Lecture Notes in Computer Science, vol 2526. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36104-9_3

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  • DOI: https://doi.org/10.1007/3-540-36104-9_3

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00044-0

  • Online ISBN: 978-3-540-36104-6

  • eBook Packages: Springer Book Archive

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