title: An automated method for lumen and media-adventitia border detection in a sequence of IVUS frames.
abstract: In this paper, we present a method for the automated detection of lumen and media-adventitia border in sequential intravascular ultrasound (IVUS) frames. The method is based on the use of deformable models. The energy function is appropriately modified and minimized using a Hopfield neural network. Proper modifications in the definition of the bias of the neurons have been introduced to incorporate image characteristics. A simulated annealing scheme is included to ensure convergence at a global minimum. The method overcomes distortions in the expected image pattern, due to the presence of calcium, employing a specialized structure of the neural network and boundary correction schemas which are based on a priori knowledge about the vessel geometry. The proposed method is evaluated using sequences of IVUS frames from 18 arterial segments, some of them indicating calcified regions. The obtained results demonstrate that our method is statistically accurate, reproducible, and capable to identify the regions of interest in sequences of IVUS frames.
authors.affiliation: Department of Computer Science, Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, GR 45110 Ioannina, Greece. marina@cs.uoi.gr
authors.forename: Marina E
authors.initials: ME
authors.lastname: Plissiti
authors.forename: Dimitrios I
authors.initials: DI
authors.lastname: Fotiadis
authors.forename: Lampros K
authors.initials: LK
authors.lastname: Michalis
authors.forename: George E
authors.initials: GE
authors.lastname: Bozios
mesh_terms: D000465:Algorithms; D002114:Calcinosis / Q000000981:diagnostic imaging*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D018084:Ultrasonography, Interventional / Q000379:methods*