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Author (up) Aura Hernandez-Sabate; Debora Gil; Petia Radeva
Title On the usefulness of supervised learning for vessel border detection in IntraVascular Imaging Type Conference Article
Year 2005 Publication Proceeding of the 2005 conference on Artificial Intelligence Research and Development Abbreviated Journal
Volume Issue Pages 67-74
Keywords classification; vessel border modelling; IVUS
Abstract IntraVascular UltraSound (IVUS) imaging is a useful tool in diagnosis of cardiac diseases since sequences completely show the morphology of coronary vessels. Vessel borders detection, especially the external adventitia layer, plays a central role in morphological measures and, thus, their segmentation feeds development of medical imaging techniques. Deterministic approaches fail to yield optimal results due to the large amount of IVUS artifacts and vessel borders descriptors. We propose using classification techniques to learn the set of descriptors and parameters that best detect vessel borders. Statistical hypothesis test on the error between automated detections and manually traced borders by 4 experts show that our detections keep within inter-observer variability.
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Corporate Author Thesis
Publisher IOS Press Place of Publication Amsterdam, The Netherlands Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes IAM;MILAB Approved no
Call Number IAM @ iam @ HGR2005c Serial 1549
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