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Statistical Segmentation of Regions of Interest on a Mammographic Image

EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 049482 (2007) Cite this article

Abstract

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.

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Authors and Affiliations

  1. Institut Fresnel, UMR-CNRS 6133, Equipe GSM, Domaine Universitaire de Saint-Jérôme, Avenue Escadrille Normandie Niemen, Marseille Cedex, 20 13397, France

    Mouloud Adel, Monique Rasigni & Salah Bourennane

  2. Service de Radiologie, Hôpital de la Timone, 27, Boulevard Jean Moulin, Marseille Cedex, 5 13385, France

    Valerie Juhan

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  1. Mouloud Adel
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  2. Monique Rasigni
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Correspondence to Mouloud Adel.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://doi.org/creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Adel, M., Rasigni, M., Bourennane, S. et al. Statistical Segmentation of Regions of Interest on a Mammographic Image. EURASIP J. Adv. Signal Process. 2007, 049482 (2007). https://doi.org/10.1155/2007/49482

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