@InProceedings{MarinaAlberti2011,
author="Marina Alberti
and Carlo Gatta
and Simone Balocco
and Francesco Ciompi
and Oriol Pujol
and Joana Silva
and Xavier Carrillo
and Petia Radeva",
editor="Jordi Vitria
and Joao Miguel Raposo
and Mario Hernandez",
title="Automatic Branching Detection in IVUS Sequences",
booktitle="5th Iberian Conference on Pattern Recognition and Image Analysis",
year="2011",
publisher="Springer Berlin Heidelberg",
address="Berlin",
volume="6669",
pages="126--133",
abstract="Atherosclerosis is a vascular pathology affecting the arterial walls, generally located in specific vessel sites, such as bifurcations. In this paper, for the first time, a fully automatic approach for the detection of bifurcations in IVUS pullback sequences is presented. The method identifies the frames and the angular sectors in which a bifurcation is visible. This goal is achieved by applying a classifier to a set of textural features extracted from each image of an IVUS pullback. A comparison between two state-of-the-art classifiers is performed, AdaBoost and Random Forest. A cross-validation scheme is applied in order to evaluate the performances of the approaches. The obtained results are encouraging, showing a sensitivity of 75\% and an accuracy of 94\% by using the AdaBoost algorithm.",
optnote="MILAB;HuPBA",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=1740), last updated on Thu, 06 Mar 2014 16:05:23 +0100",
isbn="978-3-642-21256-7",
issn="0302-9743",
doi="10.1007/978-3-642-21257-4_16"
}