%0 Journal Article
%T Intravascular  Ultrasound Tissue Characterization with Sub-class Error-Correcting  Output Codes
%A Sergio Escalera
%A Oriol Pujol
%A J. Mauri
%A Petia Radeva
%J Journal of Signal Processing Systems
%D 2009
%V 55
%N 1-3
%@ 1939-8018
%F Sergio Escalera2009
%O MILAB;HuPBA
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=1258), last updated on Wed, 16 Jan 2019 12:09:07 +0100
%X 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.
%U http://dx.doi.org/10.1007/s11265-008-0180-z
%P 35–47