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  • Research Article
  • Open Access

Statistical Segmentation of Regions of Interest on a Mammographic Image

  • 1Email author,
  • 1,
  • 1 and
  • 2
EURASIP Journal on Advances in Signal Processing20072007:049482

  • Received: 16 November 2006
  • Accepted: 13 May 2007
  • Published:


This paper deals with segmentation of breast anatomical regions, pectoral muscle, fatty and fibroglandular regions, using a Bayesian approach. This work is a part of a computer aided diagnosis project aiming at evaluating breast cancer risk and its association with characteristics (density, texture, etc.) of regions of interest on digitized mammograms. Novelty in this paper consists in applying and adapting Markov random field for detecting breast anatomical regions on digitized mammograms whereas most of previous works were focused on masses and microcalcifications. The developed method was tested on 50 digitized mammograms of the mini-MIAS database. Computer segmentation is compared to manual one made by a radiologist. A good agreement is obtained on 68% of the mini-MIAS mammographic image database used in this study. Given obtained segmentation results, the proposed method could be considered as a satisfying first approach for segmenting regions of interest in a breast.


  • Breast Cancer
  • Cancer Risk
  • Information Technology
  • Breast Cancer Risk
  • Quantum Information


Authors’ Affiliations

Institut Fresnel, UMR-CNRS 6133, Equipe GSM, Domaine Universitaire de Saint-Jérôme, Avenue Escadrille Normandie Niemen, Marseille Cedex, 20 13397, France
Service de Radiologie, Hôpital de la Timone, 27, Boulevard Jean Moulin, Marseille Cedex, 5 13385, France


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© Mouloud Adel et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.