PT Unknown
AU Aura Hernandez-Sabate
   Debora Gil
   Petia Radeva
TI On the usefulness of supervised learning for vessel border detection in IntraVascular Imaging
BT Proceeding of the 2005 conference on Artificial Intelligence Research and Development
PY 2005
BP 67
EP 74
DE classification; vessel border modelling; IVUS
AB 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.
PI Amsterdam, The Netherlands
ER