Intravascular Ultrasound Tissue Characterization with Sub-class Error-Correcting Output Codes

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 multiclass 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 S. Escalera (B) · O. Pujol · P. Radeva Centre de Visió per Computador, Campus UAB, 08193 Bellaterra Barcelona, Spain e-mail: [email protected] O. Pujol e-mail: [email protected] P. Radeva e-mail: [email protected] S. Escalera · O. Pujol · P. Radeva Department Matemàtica Aplicada i Anàlisi, UB, Gran Via 585, 08007 Barcelona, Spain J. Mauri Hospital Universityari Germans Trias i Pujol, Badalona, Spain e-mail: [email protected] method automatically characterizes different tissues, showing performance improvements over the state-ofthe-art ECOC techniques for different base classifiers. Furthermore, the combination of RF and texture-based features also shows improvements over the state-ofthe-art approaches.