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dc.contributorGarrido, Lluís
dc.contributorIgual Muñoz, Laura
dc.contributor.authorSaeta Pérez, Israel
dc.date.accessioned2014-02-12T14:26:34Z
dc.date.available2014-02-12T14:26:34Z
dc.date.issued2013-09-09
dc.identifier.urihttp://hdl.handle.net/2099.1/20738
dc.description.abstractIn this work we present a new segmentation method named Smart Cage Active Contours (SCAC) that combines a parametrized active contour framework named Cage Active Contours (CAC), based on a ne trans- formations, with Active Shape Models (ASM). Our method e ectively restricts the shapes the evolving contours can take without the need of the training images to be manually landmarked. We apply our method to segment the caudate nuclei subcortical structure of a set of 40 subjects in magnetic resonance brain images, with promising results.
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo
dc.subject.lcshImage -- Segmentation
dc.titleSmart Cage Active Contours and their application to brain image segmentation
dc.typeMaster thesis
dc.subject.lemacImatges -- Segmentació
dc.rights.accessOpen Access
dc.audience.educationlevelMàster
dc.audience.mediatorFacultat d'Informàtica de Barcelona


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Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain