ECOC random fields for lumen segmentation in radial artery IVUS sequences.
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.
Ciompi
Francesco
F
Dep. of Applied Mathematics and Analysis, University of Barcelona, Spain. f.ciompi@cvc.uab.es
Pujol
Oriol
O
Fernández-Nofrerías
Eduard
E
Mauri
Josepa
J
Radeva
Petia
P
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*