Journal of Digital Imaging

, Volume 23, Issue 5, pp 527–537 | Cite as

A Statistical Approach for Breast Density Segmentation

  • Arnau Oliver
  • Xavier Lladó
  • Elsa Pérez
  • Josep Pont
  • Erika R. E. Denton
  • Jordi Freixenet
  • Joan Martí
Article

Abstract

Studies reported in the literature indicate that the increase in the breast density is one of the strongest indicators of developing breast cancer. In this paper, we present an approach to automatically evaluate the density of a breast by segmenting its internal parenchyma in either fatty or dense class. Our approach is based on a statistical analysis of each pixel neighbourhood for modelling both tissue types. Therefore, we provide connected density clusters taking the spatial information of the breast into account. With the aim of showing the robustness of our approach, the experiments are performed using two different databases: the well-known Mammographic Image Analysis Society digitised database and a new full-field digital database of mammograms from which we have annotations provided by radiologists. Quantitative and qualitative results show that our approach is able to correctly detect dense breasts, segmenting the tissue type accordingly.

Key words

Breast tissue density statistic analysis image segmentation computerized method 

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Copyright information

© Society for Imaging Informatics in Medicine 2009

Authors and Affiliations

  • Arnau Oliver
    • 1
  • Xavier Lladó
    • 1
  • Elsa Pérez
    • 2
  • Josep Pont
    • 2
  • Erika R. E. Denton
    • 3
  • Jordi Freixenet
    • 1
  • Joan Martí
    • 1
  1. 1.Department of Computer Architecture and Technology, IIiA-IdIBGiUniversity of GironaGironaSpain
  2. 2.Department of RadiologyUniversity Hospital Josep TruetaGironaSpain
  3. 3.Department of Breast ImagingNorfolk and Norwich University Hospital NHS TrustNorwichUK

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