title: ECOC random fields for lumen segmentation in radial artery IVUS sequences.
abstract: The measure of lumen volume on radial arteries can be used to evaluate the vessel response to different vasodilators. In this paper, we present a framework for automatic lumen segmentation in longitudinal cut images of radial artery from Intravascular ultrasound sequences. The segmentation is tackled as a classification problem where the contextual information is exploited by means of Conditional Random Fields (CRFs). A multi-class classification framework is proposed, and inference is achieved by combining binary CRFs according to the Error-Correcting-Output-Code technique. The results are validated against manually segmented sequences. Finally, the method is compared with other state-of-the-art classifiers.
authors.affiliation: Dep. of Applied Mathematics and Analysis, University of Barcelona, Spain. f.ciompi@cvc.uab.es
authors.forename: Francesco
authors.initials: F
authors.lastname: Ciompi
authors.forename: Oriol
authors.initials: O
authors.lastname: Pujol
authors.forename: Eduard
authors.initials: E
authors.lastname: Fernández-Nofrerías
authors.forename: Josepa
authors.initials: J
authors.lastname: Mauri
authors.forename: Petia
authors.initials: P
authors.lastname: Radeva
mesh_terms: D000465:Algorithms; D016477:Artifacts; D003627:Data Interpretation, Statistical; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D017534:Radial Artery / Q000000981:diagnostic imaging*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D018084:Ultrasonography, Interventional / Q000379:methods*