Sciweavers

Share
CCIA
2005
Springer

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

10 years 6 months ago
On the usefulness of supervised learning for vessel border detection in IntraVascular Imaging
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. Keywords. classification, vessel border modelling, IVUS
Aura Hernandez, Debora Gil, Petia Radeva
Added 26 Jun 2010
Updated 26 Jun 2010
Type Conference
Year 2005
Where CCIA
Authors Aura Hernandez, Debora Gil, Petia Radeva
Comments (0)
books