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IVUS Tissue Characterization with Sub-class Error-Correcting Output Codes
| Content Provider | CiteSeerX |
|---|---|
| Author | Ab, Petia Radeva Ab, Sergio Escalera Josepa Mauri, C. Ab, Oriol Pujol |
| 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 Radio Frequency, texture-based, slope-based, and combined features. To deal with the classification of multiple tissues, we require the use of robust multi-class learning techniques. 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 subsets 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-theart ECOC techniques for different base classifiers and feature sets. 1. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Robust Multi-class Complex Ivus Data Set Binary Problem Feature Set Intravascular Ultrasound Problem-dependent Ecoc Design Performance Improvement New Strategy Different Tissue Ecoc Framework Multi-class Classification Task Ivus Tissue Characterization Sub-classes Information Applied Base Classifier Radio Frequency Sub-class Error-correcting Output Code Different Base Classifier Original Set Histologic Property Powerful Imaging Technique Coronary Vessel Multiple Tissue Different Subset State-of-theart Ecoc Technique |
| Content Type | Text |
