Multi-class learning for vessel characterisation in intravascular ultrasound

Abstract

In this thesis we tackle the problem of automatic characterization of human coronary vessel in IntravascularUltrasound (IVUS) image modality. The basis for the whole characterization process is machinelearning applied to multi-class problems. In all the presented approaches, the Error-Correcting Output Codes(ECOC) framework is used as central element for the design of multi-class classifiers. Two main contributionsare presented in this thesis. First, a novel method for the design of potential function for DiscriminativeRandom Fields, namely ECOC-DRF, is presented. The method is successfully applied to problems of objectclassification and segmentation in synthetic and natural images. Furthermore, ECOC-DRF is applied toobtain a robust vessel characterization in IVUS image sequences. Based on ECOC-DRF, the main regionsof the coronary artery are robustly segmented by means of a novel holistic approach, namely HoliMAb, representingthe second contribution of this thesis. The HoliMAb framework is applied to problems of lumenborder and media-adventitia border detection, achieving an error comparable with inter-observer variabilityand with state of the art methods.