@incollection{HoffmannMuellerKurzidimetal., author = {Hoffmann, Matthias and M{\"u}ller, Simone and Kurzidim, Klaus and Strobel, Norbert and Hornegger, Joachim}, title = {Robust Identification of Contrasted Frames in Fluoroscopic Images}, series = {Bildverarbeitung f{\"u}r die Medizin 2015}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2015}, publisher = {Springer Vieweg}, address = {Berlin, Heidelberg}, isbn = {978-3-662-46223-2}, doi = {10.1007/978-3-662-46224-9_6}, 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\%.}, language = {en} }