@InProceedings{SimoneBalocco2011,
author="Simone Balocco
and Carlo Gatta
and Francesco Ciompi
and Oriol Pujol
and Xavier Carrillo
and J. Mauri
and Petia Radeva",
editor="Jordi Vitria
and Joao Miguel Raposo
and Mario Hernandez",
title="Combining Growcut and Temporal Correlation for IVUS Lumen Segmentation",
booktitle="5th Iberian Conference on Pattern Recognition and Image Analysis",
year="2011",
publisher="Springer Berlin Heidelberg",
address="Berlin",
volume="6669",
pages="556--563",
abstract="The assessment of arterial luminal area, performed by IVUS analysis, is a clinical index used to evaluate the degree of coronary artery disease. In this paper we propose a novel approach to automatically segment the vessel lumen, which combines model-based temporal information extracted from successive frames of the sequence, with spatial classification using the Growcut algorithm. The performance of the method is evaluated by an in vivo experiment on 300 IVUS frames. The automatic and manual segmentation performances in general vessel and stent frames are comparable. The average segmentation error in vessel, stent and bifurcation frames are 0.17{\textpm}0.08 mm, 0.18{\textpm}0.07 mm and 0.31{\textpm}0.12 mm respectively.",
optnote="MILAB;HuPBA",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=1741), last updated on Thu, 06 Mar 2014 16:07:59 +0100",
isbn="978-3-642-21256-7",
issn="0302-9743",
doi="10.1007/978-3-642-21257-4_69"
}