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Robust Identification of Contrasted Frames in Fluoroscopic Images

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%.


  • Support Vector Machine
  • Contrast Agent
  • Left Atrium
  • Digital Subtraction Angiography
  • Transcatheter Aortic Valve Implantation

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  • DOI: 10.1007/978-3-662-46224-9_6
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  1. Calkins H, Brugada J, Packer D, et al. HRS/EHRA/ECAS expert consensus statement on catheter and surgical ablation of atrial fibrillation: recommendations for Personnel, Policy, Procedures and Follow-Up. Europace. 2007;9(6):335–79.

    CrossRef  Google Scholar 

  2. Dilling-Boer D, van der Merwe N, Adams J, et al. Ablation of focally induced atrial fibrillation. J Cardiovasc Electrophysiol. 2004;15(2):200–5.

    CrossRef  Google Scholar 

  3. Brost A, Raab J, Kleinoeder A, et al. Medizinische Bildverarbeitung f¨ur die minimalinvasive Behandlung von Vorhofflimmern. DZKF. 2013;17(6):36–41.

    Google Scholar 

  4. Bourier F, Vukajlovic D, Brost A, et al. Pulmonary vein isolation supported by MRI-derived 3D-augmented biplane fluoroscopy: a feasibility study and a quantitative analysis of the accuracy of the technique. J Cardiovasc Electrophysiol. 2007;115:3057–63.

    Google Scholar 

  5. Thivierge-Gaulin D, Chou CR, Kiraly A, et al. 3D-2D registration based on meshderived image bisection. Lect Notes Computer Sci. 2012; p. 70–78.

    Google Scholar 

  6. Zhao X, Miao S, Du L, et al. Robust 2-D/3-D registration of CT volumes with contrast-enhanced x-ray sequences in electro-physiology based on a weighted similarity measure and sequential subspace optimization. Proc ICASSP. 2013; p. 934–8.

    Google Scholar 

  7. Condurache A, Aach T, Eck K, et al. Fast detection and processing of arbitrary contrast agent injections in coronary angiopgraphy and fluoroscopy. Procs BVM. 2004; p. 5–9.

    Google Scholar 

  8. Chen T, Funka-Lea G, Comaniciu D. Robust and fast contrast inflow detection for 2D x-ray fluoroscopy. Lect Notes Computer Sci. 2011; p. 243–50.

    Google Scholar 

  9. Liao R, You W, Liu Y, et al. Integrated spatiotemporal analysis for automatic contrast agent inflow detection on angiography and fluoroscopy during transcatheter aortic valve implantation. Med Phys. 2013;40(4).

    Google Scholar 

  10. Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273–97.

    MATH  Google Scholar 

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Correspondence to Matthias Hoffmann .

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© 2015 Springer-Verlag Berlin Heidelberg

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Hoffmann, M., Müller, S., Kurzidim, K., Strobel, N., Hornegger, J. (2015). Robust Identification of Contrasted Frames in Fluoroscopic Images. In: Handels, H., Deserno, T., Meinzer, HP., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2015. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg.

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  • Publisher Name: Springer Vieweg, Berlin, Heidelberg

  • Print ISBN: 978-3-662-46223-2

  • Online ISBN: 978-3-662-46224-9

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