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-ofthe- art classifiers.
Ecoc random fields for lumen segmentation in radial artery ivus sequences
F. Ciompi, O. Pujol, E. Fernandez-Nofrerias, J. Mauri and P. Radeva
Medical Image Computing and Computer-Assisted Intervention 2009:869-876.