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A Comparison of Breast Tissue Classification Techniques

  • Arnau Oliver
  • Jordi Freixenet
  • Robert Martí
  • Reyer Zwiggelaar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)

Abstract

It is widely accepted in the medical community that breast tissue density is an important risk factor for the development of breast cancer. Thus, the development of reliable automatic methods for classification of breast tissue is justified and necessary. Although different approaches in this area have been proposed in recent years, only a few are based on the BIRADS classification standard. In this paper we review different strategies for extracting features in tissue classification systems, and demonstrate, not only the feasibility of estimating breast density using automatic computer vision techniques, but also the benefits of segmentation of the breast based on internal tissue information. The evaluation of the methods is based on the full MIAS database classified according to BIRADS categories, and agreement between automatic and manual classification of 82% was obtained.

Keywords

Breast Tissue Breast Density Fractal Approach Digital Mammogram Breast Area 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Arnau Oliver
    • 1
  • Jordi Freixenet
    • 1
  • Robert Martí
    • 1
  • Reyer Zwiggelaar
    • 2
  1. 1.Institute of Informatics and ApplicationsUniversity of GironaGironaSpain
  2. 2.Department of Computer ScienceUniversity of WalesAberystwythUK

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