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13 April 2018 Breast mass segmentation in mammograms combining fuzzy c-means and active contours
Marwa Hmida, Kamel Hamrouni, Basel Solaiman, Sana Boussetta
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Proceedings Volume 10696, Tenth International Conference on Machine Vision (ICMV 2017); 106960R (2018) https://doi.org/10.1117/12.2310196
Event: Tenth International Conference on Machine Vision, 2017, Vienna, Austria
Abstract
Segmentation of breast masses in mammograms is a challenging issue due to the nature of mammography and the characteristics of masses. In fact, mammographic images are poor in contrast and breast masses have various shapes and densities with fuzzy and ill-defined borders. In this paper, we propose a method based on a modified Chan-Vese active contour model for mass segmentation in mammograms. We conduct the experiment on mass Regions of Interest (ROI) extracted from the MIAS database. The proposed method consists of mainly three stages: Firstly, the ROI is preprocessed to enhance the contrast. Next, two fuzzy membership maps are generated from the preprocessed ROI based on fuzzy C-Means algorithm. These fuzzy membership maps are finally used to modify the energy of the Chan-Vese model and to perform the final segmentation. Experimental results indicate that the proposed method yields good mass segmentation results.
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Marwa Hmida, Kamel Hamrouni, Basel Solaiman, and Sana Boussetta "Breast mass segmentation in mammograms combining fuzzy c-means and active contours", Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106960R (13 April 2018); https://doi.org/10.1117/12.2310196
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KEYWORDS
Fuzzy logic
Image segmentation
Mammography
Breast
Databases
Breast cancer
Computer aided diagnosis and therapy
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