Mammographic mass segmentation using fuzzy contours. (October 2018)

• Record Type: Journal Article
• Authors: Hmida, Marwa; Hamrouni, Kamel; Solaiman, Basel; Boussetta, Sana
• Is Part Of: Computer methods and programs in biomedicine. Volume 164(2018)
• Page Start: 131
• Page End: 142
• Publication Date: 2018-10
• Subjects: Mass segmentation -- Mammography -- Active contours -- Fuzzy contours; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.07.005
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: An automatic system for mass segmentation in regions of interest extracted from mammograms. Fuzzy contours formalism allow dealing with imprecision inherent to contour localization. Introducing fuzzy contours as a constraint which restrains the evolution of the Chan–Vese model in order to properly extract the accurate mass contour. Reducing false positives caused by inhomogeneity in region of interest tissue. The proposed method provides an accurate and reliable results compared to ground truth images and previous work. Abstract: Background and Objective : Accurate mass segmentation in mammographic images is a critical requirement for computer-aided diagnosis systems since it allows accurate feature extraction and thus improves classification precision. Methods : In this paper, a novel automatic breast mass segmentation approach is presented. This approach consists of mainly three stages: contour initialization applied to a given region of interest; construction of fuzzy contours and estimation of fuzzy membership maps of different classes in the considered image; integration of these maps in the Chan–Vese model to get a fuzzy-energy based model that is used for final delineation of mass. Results : The proposed approach is evaluated using mass regions of interest extracted from the mini-MIAS database. The experimental results show that the proposed method achieves an average true positive rate of 91.12% with a precision of 88.08%. Conclusions : The achieved results show high accuracy in breast mass segmentation when compared to manually annotated ground truth and to other methods from the literature.