Robust Identification of Contrasted Frames in Fluoroscopic Images

  • Matthias Hoffmann
  • Simone Müller
  • Klaus Kurzidim
  • Norbert Strobel
  • Joachim Hornegger
Conference paper
Part of the Informatik aktuell book series (INFORMAT)


For automatic registration of 3-D models of the left atrium to fluoroscopic images, a reliable classification of images containing contrast agent is necessary. Inspired by previous approaches on contrast agent detection, we propose a learning-based framework which is able to classify contrasted frames more robustly than previous methods, Furthermore, we performed a quantitative evaluation on a clinical data set consisting of 34 angiographies. Our learning-based approach reached a classification rate of 79.5%. The beginning of a contrast injection was detected correctly in 79.4%.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Matthias Hoffmann
    • 1
  • Simone Müller
    • 1
  • Klaus Kurzidim
    • 2
  • Norbert Strobel
    • 3
  • Joachim Hornegger
    • 1
    • 4
  1. 1.pattern Recognition LabFriedrich-Alexander-Universität Erlangen-NürnbergErlangenDeutschland
  2. 2.Klinik für HerzrhythmusstörungenKrankenhaus Barmherzige BrüderRegensburgDeutschland
  3. 3.Siemens AGHealthcareForchheimDeutschland
  4. 4.Erlangen Graduate School in Advanced Optical Technologies (SAOT)ErlangenDeutschland

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