Abstract
This paper presents a method for breast density classification. Local ternary pattern operators are employed to model the appearance of the fibroglandular disk region instead of the whole breast region as the majority of current studies have done. The Support Vector Machine classifier is used to perform the classification and a stratified ten-fold cross-validation scheme is employed to evaluate the performance of the method. The proposed method achieved 82.33% accuracy which is comparable with some of the best methods in the literature based on the same dataset and evaluation scheme.
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Acknowledgments
This research was undertaken as part of the Decision Support and Information Management System for Breast Cancer (DESIREE) project. The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 690238.
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Rampun, A., Morrow, P., Scotney, B., Winder, J. (2017). Breast Density Classification Using Local Ternary Patterns in Mammograms. In: Karray, F., Campilho, A., Cheriet, F. (eds) Image Analysis and Recognition. ICIAR 2017. Lecture Notes in Computer Science(), vol 10317. Springer, Cham. https://doi.org/10.1007/978-3-319-59876-5_51
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DOI: https://doi.org/10.1007/978-3-319-59876-5_51
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