Abstract
This paper presents a preliminary quantitative study for breast cancer risk assessment in mammography using mathematical operators called Local Ternary Patterns. The study covers three different mapping patterns namely uniform (‘u2’), nonuniform (‘ri’) and a combination of uniform and nonuniform (‘riu2’). These patterns are used as texture features to model the appearance of breast density within the fibroglandular disk area. Subsequently, the Support Vector Machine is employed as a classification approach and initial results suggest that the mapping pattern ‘riu2’ outperforms the others.
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References
Oliveira, A., Fonseca-Moutinho, J.A., Pereira, M., Freire, M.M.: A survey of the methods used to classify breast density in mammograms and ultrasound images. http://www.di.ubi.pt/~mario/Angela1.pdf. Accessed 3 Feb 2017
Suckling, J., et al.: The mammographic image analysis society digital mammogram database. In: Proceedings Excerpta Medica International Congress Series, pp. 375–378 (1994)
Hadid, A., Pietikainen, M.K., Zhao, G., Ahonen, T.: Computer Vision Using Local Binary Patterns, pp. 13–47. Springer, London (2011)
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.J., Scotney, B.W., Winder, J. (2018). A Quantitative Study of Local Ternary Patterns for Risk Assessment in Mammography. In: Chen, YW., Tanaka, S., Howlett, R., Jain, L. (eds) Innovation in Medicine and Healthcare 2017. KES-InMed 2018 2017. Smart Innovation, Systems and Technologies, vol 71. Springer, Cham. https://doi.org/10.1007/978-3-319-59397-5_31
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DOI: https://doi.org/10.1007/978-3-319-59397-5_31
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