Intravascular ultrasound uissue characterization with sub-class error-correcting output codes

Sergio Escalera, Oriol Pujol, Josepa Mauri, Petia Radeva

    Research output: Contribution to journalArticleResearchpeer-review

    25 Citations (Scopus)


    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. © 2008 Springer Science+Business Media, LLC.
    Original languageEnglish
    Pages (from-to)35-47
    JournalJournal of Signal Processing Systems
    Issue number1-3
    Publication statusPublished - 1 Apr 2009


    • Embedding of dichotomies
    • Error-correcting output codes
    • Intravascular ultrasound
    • Multi-class classification
    • Sub-classes


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