title: Finding similar 2D X-ray coronary angiograms.
abstract: 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.
authors.affiliation: IBM Almaden Research Center, San Jose, CA, USA.
authors.forename: Tanveer
authors.initials: T
authors.lastname: Syeda-Mahmood
authors.forename: Fei
authors.initials: F
authors.lastname: Wang
authors.forename: R
authors.initials: R
authors.lastname: Kumar
authors.forename: D
authors.initials: D
authors.lastname: Beymer
authors.forename: Y
authors.initials: Y
authors.lastname: Zhang
authors.forename: Robert
authors.initials: R
authors.lastname: Lundstrom
authors.forename: Edward
authors.initials: E
authors.lastname: McNulty
mesh_terms: D000465:Algorithms; D017023:Coronary Angiography / Q000379:methods*; D003324:Coronary Artery Disease / Q000000981:diagnostic imaging*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique