In clinical practice, physicians often exploit previously observed patterns in coronary angiograms from similar patients to quickly assess the state of the disease in a current patient. These assessments involve visually observed features such as the distance of a junction from the root and the tortuosity of the arteries. In this paper, we show how these visual features can be automatically extracted from coronary artery images and used for finding similar coronary angiograms from a database. Testing on a large collection has shown the method finds clinically similar coronary angiograms from patients with similar clinical history.
CITATION STYLE
Syeda-Mahmood, T., Wang, F., Kumar, R., Beymer, D., Zhang, Y., Lundstrom, R., & McNulty, E. (2012). Finding similar 2D X-ray coronary angiograms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7512 LNCS, pp. 501–508). Springer Verlag. https://doi.org/10.1007/978-3-642-33454-2_62
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