Statistical Segmentation of Regions of Interest on a Mammographic Image
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Research Article Statistical Segmentation of Regions of Interest on a Mammographic Image Mouloud Adel,1 Monique Rasigni,1 Salah Bourennane,1 and Valerie Juhan2 1 Institut
Fresnel, UMR-CNRS 6133, Equipe GSM, Domaine Universitaire de Saint-J´erˆome, Avenue Escadrille Normandie Niemen, 13397 Marseille Cedex 20, France 2 Service de Radiologie, Hˆ opital de la Timone, 27, Boulevard Jean Moulin, 13385 Marseille Cedex 5, France Received 16 November 2006; Revised 11 April 2007; Accepted 13 May 2007 Recommended by Jiri Jan 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. Copyright © 2007 Mouloud Adel et al. 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.
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INTRODUCTION
Breast cancer is the leading cause of death among all cancers for middle-aged women. Currently it affects one woman out of eight and an increase of this rate in the nearest future is expected. For the last 40 years, extensive means have been devoted to tackling this disease but without the expected success. Efforts are now focused on early detection and prevention. It is now known that screening programs reduce the mortality rate of about 30% for middle-aged women. At present, mammography is the current standard for early breast cancer detection. Mammographic images are difficult to analyse due to wide variation of anatomical patterns of each breast. One important task for radiologists when reading mammograms consists in evaluating the proportion of fatty and fibroglandular tissue with respect to the whole breast. Mammographic density is known to be an important indicator of breast cancer risk. There are four metrics which are used in practice to relate the mammographic parenchymal patterns and the risk of breast cancer, namely: Wolfe’s four parenchymal patterns [1], Boyd’s six class categories [2], BI-RADS [3], and Tab´ar’s five patterns [4]. The comparative study of these four approaches on MIAS database [5] in particular has been re-
ported in [6]. In first studies devo
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