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- W1539969231 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." @default.
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- W1539969231 date "2009-01-01" @default.
- W1539969231 modified "2023-10-05" @default.
- W1539969231 title "ECOC Random Fields for Lumen Segmentation in Radial Artery IVUS Sequences" @default.
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- W1539969231 doi "https://doi.org/10.1007/978-3-642-04271-3_105" @default.
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