Mammographic Segmentation and Risk Classification Using a Novel Binary Model Based Bayes Classifier

  • Wenda He
  • Erika R. E. Denton
  • Reyer Zwiggelaar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7361)


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.


Mammographic Density Breast Density Binary Model Percentage Mammographic Density Mammographic Image 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Wenda He
    • 1
  • Erika R. E. Denton
    • 2
  • Reyer Zwiggelaar
    • 1
  1. 1.Department of Computer ScienceAberystwyth UniversityAberystwythUK
  2. 2.Department of RadiologyNorfolk & Norwich University HospitalNorwichUK

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