Towards cardiovascular risk stratification using imaging data.
In spite of the advancement and proliferation of cardiovascular imaging data, the rate of deaths due to unpredicted heart attack remains high. Thus, it becomes imperative to develop novel computational tools to mine quantitative parameters from imaging data for early detection and diagnosis of asymptomatic cardiovascular disease. In this paper, we present our progress towards developing a computational framework to mine cardiac imaging data and provide quantitative measures for developing a new risk assessment method. Specifically, we present computational methods developed for the detection of coronary calcification and segmentation of thoracic aorta in non-contrast cardiac computed tomography, and detection of neovessels in plaques in intravascular ultrasound imaging data.
Kakadiaris
I A
IA
Department of Computer Science, University of Houston, 4800 Calhoun Rd., Houston, TX 77204-3010 USA. ioannisk@uh.edu
Kurkure
U
U
Mendizabal-Ruiz
E G
EG
Naghavi
M
M
D001013:Aorta, Thoracic / Q000000981:diagnostic imaging; D002114:Calcinosis / Q000000981:diagnostic imaging / Q000453:epidemiology; D002318:Cardiovascular Diseases / Q000000981:diagnostic imaging / Q000453:epidemiology* / Q000401:mortality; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D009203:Myocardial Infarction / Q000453:epidemiology / Q000401:mortality; D054555:National Heart, Lung, and Blood Institute (U.S.); D018570:Risk Assessment / Q000379:methods; D014057:Tomography, X-Ray Computed / Q000379:methods; D014481:United States / Q000453:epidemiology