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2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
IVUS tissue characterization with sub-class error-correcting output codes
Anchorage, AK, USA
June 23-June 28
ISBN: 978-1-4244-2339-2
Sergio Escalera, Centre de Visióo per Computador, Campus UAB, 08193 Bellaterra (Barcelona), Spain
Oriol Pujola, Dept. Matematica Aplicada i Analisi, UB, Gran Via 585, 08007, Barcelona, Spain
Josepa Mauri, Hospital Universitari Germans Trias i Pujol, Badalona, Spain
Petia Radeva, Dept. Matematica Aplicada i Analisi, UB, Gran Via 585, 08007, Barcelona, Spain
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-the-art ECOC techniques for different base classifiers and feature sets.
Citation:
Sergio Escalera, Oriol Pujola, Josepa Mauri, Petia Radeva, "IVUS tissue characterization with sub-class error-correcting output codes," cvprw, pp.1-8, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008
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