Journal of Signal Processing Systems

, Volume 55, Issue 1–3, pp 35–47 | Cite as

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

  • Sergio Escalera
  • Oriol Pujol
  • Josepa Mauri
  • Petia Radeva
Article

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.

Keywords

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

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Sergio Escalera
    • 1
    • 2
  • Oriol Pujol
    • 1
    • 2
  • Josepa Mauri
    • 3
  • Petia Radeva
    • 1
    • 2
  1. 1.Centre de Visió per ComputadorBellaterra BarcelonaSpain
  2. 2.Department Matemàtica Aplicada i AnàlisiBarcelonaSpain
  3. 3.Hospital Universityari Germans Trias i PujolBadalonaSpain

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