Mammographic Ellipse Modelling Towards Birads Density Classification

  • Minu George
  • Andrik Rampun
  • Erika Denton
  • Reyer Zwiggelaar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9699)

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.

Keywords

Breast density modelling Blob and ellipse detection 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Minu George
    • 1
  • Andrik Rampun
    • 1
  • Erika Denton
    • 2
  • Reyer Zwiggelaar
    • 1
  1. 1.Department of Computer ScienceAberystwyth UniversityAberystwythUK
  2. 2.Department of RadiologyNorfolk and Norwich University HospitalNorwichUK

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