Abstract
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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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. https://doi.org/10.1007/978-3-662-46224-9_6
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DOI: https://doi.org/10.1007/978-3-662-46224-9_6
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