@Article{FrancescoCiompi2012,
author="Francesco Ciompi
and Oriol Pujol
and Carlo Gatta
and Marina Alberti
and Simone Balocco
and Xavier Carrillo
and J. Mauri
and Petia Radeva",
title="HoliMab: A Holistic Approach for Media-Adventitia Border Detection in Intravascular Ultrasound",
journal="Medical Image Analysis",
year="2012",
volume="16",
number="6",
pages="1085--1100",
optkeywords="Media--Adventitia border detection",
optkeywords="Intravascular ultrasound",
optkeywords="Multi-Scale Stacked Sequential Learning",
optkeywords="Error-correcting output codes",
optkeywords="Holistic segmentation",
abstract="We present a fully automatic methodology for the detection of the Media-Adventitia border (MAb) in human coronary artery in Intravascular Ultrasound (IVUS) images. A robust border detection is achieved by means of a holistic interpretation of the detection problem where the target object, i.e. the media layer, is considered as part of the whole vessel in the image and all the relationships between tissues are learnt. A fairly general framework exploiting multi-class tissue characterization as well as contextual information on the morphology and the appearance of the tissues is presented. The methodology is (i) validated through an exhaustive comparison with both Inter-observer variability on two challenging databases and (ii) compared with state-of-the-art methods for the detection of the MAb in IVUS. The obtained averaged values for the mean radial distance and the percentage of area difference are 0.211 mm and 10.1\%, respectively. The applicability of the proposed methodology to clinical practice is also discussed.",
optnote="MILAB;HuPBA",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=1995), last updated on Thu, 13 Mar 2014 13:31:24 +0100",
doi="10.1016/j.media.2012.06.008",
opturl="http://dx.doi.org/10.1016/j.media.2012.06.008"
}