title: Image analysis techniques for automated IVUS contour detection.
abstract: Intravascular ultrasound (IVUS) constitutes a valuable technique for the diagnosis of coronary atherosclerosis. The detection of lumen and media-adventitia borders in IVUS images represents a necessary step towards the reliable quantitative assessment of atherosclerosis. In this work, a fully automated technique for the detection of lumen and media-adventitia borders in IVUS images is presented. This comprises two different steps for contour initialization: one for each corresponding contour of interest and a procedure for the refinement of the detected contours. Intensity information, as well as the result of texture analysis, generated by means of a multilevel discrete wavelet frames decomposition, are used in two different techniques for contour initialization. For subsequently producing smooth contours, three techniques based on low-pass filtering and radial basis functions are introduced. The different combinations of the proposed methods are experimentally evaluated in large datasets of IVUS images derived from human coronary arteries. It is demonstrated that our proposed segmentation approaches can quickly and reliably perform automated segmentation of IVUS images.
authors.affiliation: Informatics and Telematics Institute (ITI)/ Centre for Research and Technology Hellas (CERTH), Thessaloniki, Greece. mpapad@iti.gr
authors.forename: Maria
authors.initials: M
authors.lastname: Papadogiorgaki
authors.forename: Vasileios
authors.initials: V
authors.lastname: Mezaris
authors.forename: Yiannis S
authors.initials: YS
authors.lastname: Chatzizisis
authors.forename: George D
authors.initials: GD
authors.lastname: Giannoglou
authors.forename: Ioannis
authors.initials: I
authors.lastname: Kompatsiaris
mesh_terms: D003324:Coronary Artery Disease / Q000000981:diagnostic imaging*; D003331:Coronary Vessels / Q000000981:diagnostic imaging*; D019160:Endosonography / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D018084:Ultrasonography, Interventional / Q000379:methods*