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Multi-class learning for vessel characterisation in intravascular ultrasound
Ciompi, Francesco

Data: 2014
Resum: 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.
Nota: Advisors: Petia Radeva, Oriol Pujol. Date and location of PhD thesis defense: 5 July 2012, Universitat de Barcelona
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Llengua: Anglès
Document: other ; abstract ; publishedVersion
Matèria: Medical image analysis ; Computer vision ; Intravascular ultrasound ; Graphical models
Publicat a: ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 13, Núm. 2 (2014) , p. 47-48, ISSN 1577-5097

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DOI: 10.5565/rev/elcvia.625

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