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. © 2008 Springer Science+Business Media, LLC.
Original language | English |
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Pages (from-to) | 35-47 |
Journal | Journal of Signal Processing Systems |
Volume | 55 |
Issue number | 1-3 |
DOIs | |
Publication status | Published - 1 Apr 2009 |
Keywords
- Embedding of dichotomies
- Error-correcting output codes
- Intravascular ultrasound
- Multi-class classification
- Sub-classes