@Article{SergioEscalera2009,
author="Sergio Escalera
and Oriol Pujol
and J. Mauri
and Petia Radeva",
title="Intravascular  Ultrasound Tissue Characterization with Sub-class Error-Correcting  Output Codes",
journal="Journal of Signal Processing Systems",
year="2009",
volume="55",
number="1-3",
pages="35--47",
abstract="Intravascular ultrasound (IVUS) represents a powerful imaging technique to explore coronary vessels and to study their morphology and histologic properties. In this paper, we characterize different tissues based on radial frequency, texture-based, and combined features. To deal with the classification of multiple tissues, we require the use of robust multi-class learning techniques. In this sense, error-correcting output codes (ECOC) show to robustly combine binary classifiers to solve multi-class problems. In this context, we propose a strategy to model multi-class classification tasks using sub-classes information in the ECOC framework. The new strategy splits the classes into different sub-sets according to the applied base classifier. Complex IVUS data sets containing overlapping data are learnt by splitting the original set of classes into sub-classes, and embedding the binary problems in a problem-dependent ECOC design. The method automatically characterizes different tissues, showing performance improvements over the state-of-the-art ECOC techniques for different base classifiers. Furthermore, the combination of RF and texture-based features also shows improvements over the state-of-the-art approaches.",
optnote="MILAB;HuPBA",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=1258), last updated on Wed, 16 Jan 2019 12:09:07 +0100",
issn="1939-8018",
doi="10.1007/s11265-008-0180-z"
}