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%.
Keywords
- Support Vector Machine
- Contrast Agent
- Left Atrium
- Digital Subtraction Angiography
- Transcatheter Aortic Valve Implantation
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.