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A Statistical Approach for Breast Density Segmentation

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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.

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Acknowledgements

This work was supported by the Ministerio de Educación y Ciencia of Spain under Grant TIN2007-60553, by the UdG under Grant IdIBGi-UdG and by CIRIT and CUR of DIUiE of Generalitat de Catalunya under Grant 2008SALUT00029.

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Authors and Affiliations

  1. Department of Computer Architecture and Technology, IIiA-IdIBGi, University of Girona, Campus Montilivi, Ed. P-IV, 17071, Girona, Spain

    Arnau Oliver, Xavier Lladó, Jordi Freixenet & Joan Martí

  2. Department of Radiology, University Hospital Josep Trueta, Carretera de França, 17007, Girona, Spain

    Elsa Pérez & Josep Pont

  3. Department of Breast Imaging, Norfolk and Norwich University Hospital NHS Trust, Colney Lane, Norwich, NR4 7UY, UK

    Erika R. E. Denton

Authors
  1. Arnau Oliver
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  2. Xavier Lladó
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  3. Elsa Pérez
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  4. Josep Pont
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  5. Erika R. E. Denton
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  6. Jordi Freixenet
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  7. Joan Martí
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Corresponding author

Correspondence to Arnau Oliver.

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Oliver, A., Lladó, X., Pérez, E. et al. A Statistical Approach for Breast Density Segmentation. J Digit Imaging 23, 527–537 (2010). https://doi.org/10.1007/s10278-009-9217-5

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  • DOI: https://doi.org/10.1007/s10278-009-9217-5

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