Abstract
It has been shown that breast density and parenchymal patterns are important indicators in mammographic risk assessment. In addition, the accuracy of detecting abnormalities depends strongly on the structure and density of breast tissue. As such, mammographic parenchymal modelling and the related density estimation or classification are playing an important role in computer aided diagnosis. In this paper, we present a novel approach to the modelling of parenchymal tissue, which is directly linked to Tabar’s normal breast tissue representation and based on the multi-scale distribution of dark ellipses, and the complementary distribution of bright ellipses which represent dense tissue. Our initial evaluation is based on the full MIAS database. We provide analysis of the separation between the Birads density classes, which indicates significant differences and a way towards automatic Birads based density classification.
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George, M., Rampun, A., Denton, E., Zwiggelaar, R. (2016). Mammographic Ellipse Modelling Towards Birads Density Classification. In: Tingberg, A., Lång, K., Timberg, P. (eds) Breast Imaging. IWDM 2016. Lecture Notes in Computer Science(), vol 9699. Springer, Cham. https://doi.org/10.1007/978-3-319-41546-8_53
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DOI: https://doi.org/10.1007/978-3-319-41546-8_53
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