Abstract
Clinical research has shown that the sensitivity of mammography is significantly reduced by increased breast density, which can mask some tumours due to dense fibroglandular tissue. In addition, there is a clear correlation between the overall breast density and mammographic risk. We present an automatic mammographic density segmentation approach using a novel binary model based Bayes classifier. The Mammographic Image Analysis Society (MIAS) database was used in a quantitative and qualitative evaluation. Visual assessment on the segmentation results indicated a good and consistent extraction of mammographic density. With respect to mammographic risk classification, substantial agreements were found between the classification results and ground truth provided by expert screening radiologists. Classification accuracies were 85% and 78% in Tabár and Breast Imaging Reporting and Data System (Birads) categories, respectively; whilst in the corresponding low and high categories, the classification accuracies were 93% and 88% for Tabár and Birads, respectively.
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He, W., Denton, E.R.E., Zwiggelaar, R. (2012). Mammographic Segmentation and Risk Classification Using a Novel Binary Model Based Bayes Classifier. In: Maidment, A.D.A., Bakic, P.R., Gavenonis, S. (eds) Breast Imaging. IWDM 2012. Lecture Notes in Computer Science, vol 7361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31271-7_6
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DOI: https://doi.org/10.1007/978-3-642-31271-7_6
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