Mammography Taxonomy for the Improvement of Lesion Detection Rates

  • Daniel Howard
  • Simon C. Roberts
  • László Tabár
Conference paper


This work in this paper aims to assist CAD by classifying the context in which lesions can occur. This is achieved by a mammogram taxonomy system which automatically classifies breast parenchyma. The classification engine is an artificial neural network which successfully forms narrow classes to capture the subtleties in parenchyma variation. This paper presents the result of a series of experiments that digitized 628 mammograms at 50 µm from the mammographic archive at Falun Central Hospital.


False Alarm Textural Feature Mammographic Density Narrow Classis Classification Confidence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Daniel Howard
    • 1
  • Simon C. Roberts
    • 1
  • László Tabár
    • 2
  1. 1.QinetiQ Software Evolution CentreMalvernUK
  2. 2.Falun Central HospitalFalunSweden

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