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A Boosting-Based Approach to Refine the Segmentation of Masses in Mammography

  • Mario Molinara
  • Claudio Marrocco
  • Francesco Tortorella
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8157)

Abstract

In this paper we present an algorithm for finding an accurate estimate of the contour of masses in mammograms. We assume that a rough estimate of the region containing the mass is known: in particular it is available the location of an area inside the mass (core) and a closed curve beyond which the mass does not extend. The proposed method employs a boosting-based classifier trained on the core and on a background region beyond the external contour, so that it provides an accurate estimate of the mass contour by classifying unlabeled pixels between the core and the external contour. The proposed approach is useful not only for automatic localization of mass contour, but also as a powerful tool during annotation of mammograms, given that an user provides interactively an estimate for the core and the external contour of the mass. The approach has been verified on a set of mammograms showing very encouraging results.

Keywords

Mammography Mass Segmentation Boosting 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mario Molinara
    • 1
  • Claudio Marrocco
    • 1
  • Francesco Tortorella
    • 1
  1. 1.Dept. of Electrical and Information EngineeringUniversità degli Studi di Cassino e del Lazio MeridionaleCassinoItaly

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