Statistical strategy for anisotropic adventitia modelling in IVUS.
Vessel plaque assessment by analysis of intravascular ultrasound sequences is a useful tool for cardiac disease diagnosis and intervention. Manual detection of luminal (inner) and media-adventitia (external) vessel borders is the main activity of physicians in the process of lumen narrowing (plaque) quantification. Difficult definition of vessel border descriptors, as well as, shades, artifacts, and blurred signal response due to ultrasound physical properties trouble automated adventitia segmentation. In order to efficiently approach such a complex problem, we propose blending advanced anisotropic filtering operators and statistical classification techniques into a vessel border modelling strategy. Our systematic statistical analysis shows that the reported adventitia detection achieves an accuracy in the range of interobserver variability regardless of plaque nature, vessel geometry, and incomplete vessel borders.
Gil
Debora
D
Computer Science Department, Computer Vision Center, Universidad Autonoma de Barcelona, Spain. debora@cvc.uab.es
Hernández
Aura
A
Rodriguez
Oriol
O
Mauri
Josepa
J
Radeva
Petia
P
D000465:Algorithms; D016880:Anisotropy; D001185:Artificial Intelligence; D001808:Blood Vessels / Q000000981:diagnostic imaging*; D003198:Computer Simulation; D003238:Connective Tissue / Q000000981:diagnostic imaging*; D003627:Data Interpretation, Statistical; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D008955:Models, Cardiovascular; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D018084:Ultrasonography, Interventional / Q000379:methods*