Abstract
This paper presents a method for automated mammographic risk classification based on breast density estimation in mammograms. The overall profile of breast tissue density is represented using a topographic map, which is a hierarchical representation, obtained from the upper level sets of an image. A shape tree is constructed to describe the topological and geometrical structure of the shapes (i.e. connected components) within the topographic map. Two properties, saliency and independency, are defined to detect shapes of interest (i.e. dense regions) based on the shape tree. A density map is further generated focusing on dense regions, which provides a quantitative description of breast density. Finally, mammographic risk classification is performed based on the breast density measures derived from the density map. The validity of this method is evaluated using the full MIAS database and a large dataset taken from the DDSM database. A high agreement with expert radiologists is indicated according to the BIRADS density classification. The obtained classification accuracies are 76.01% and 81.22%, respectively.
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Chen, Z., Oliver, A., Denton, E., Zwiggelaar, R. (2013). Automated Mammographic Risk Classification Based on Breast Density Estimation. In: Sanches, J.M., Micó, L., Cardoso, J.S. (eds) Pattern Recognition and Image Analysis. IbPRIA 2013. Lecture Notes in Computer Science, vol 7887. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38628-2_28
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DOI: https://doi.org/10.1007/978-3-642-38628-2_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38627-5
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