On the usefulness of supervised learning for vessel border detection in IntraVascular Imaging

Authors:
Aura Hernàndez;Debora Gil;Petia Radeva
Affiliations:
Centre de Visió per Computador;Centre de Visió per Computador;Centre de Visió per Computador and Dept. de Ciències de la Computació UAB
Venue:
Proceedings of the 2005 conference on Artificial Intelligence Research and Development
Year:
2005

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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.