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dc.contributor.authorJaouen, Vincent
dc.contributor.authorGonzález-Gutiérrez, Paulo
dc.contributor.authorStute, Simon
dc.contributor.authorGuilloteau, Denis
dc.contributor.authorChalon, Sylvie
dc.contributor.authorBuvat, Irene
dc.contributor.authorTauber, Clovis
dc.description.abstractIn this paper, we generalize the gradient vector flow field to vector-valued images. We base our method on the definition of a structure tensor that is calculated according to a blind estimation of contrast in the different channels and that exploits the whole spatio-spectral information, hence reducing sensitivity to noise and better defining orientations of the force field. The resulting field takes profit of both magnitude and direction of the vector-valued gradient. Applied to biological volume delineation in 3D dynamic Positron Emission Tomography (PET) imaging, we validate our method on realistic Monte Carlo simulations of numerical phantoms and present results on real dynamic PET data. Performances observed on such images confirm the potential of the proposed active surface approach for vector-valued data.es_CL
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile*
dc.sourceTraitement du Signal, 31(1-2), 9-38es_CL
dc.subject3D segmentationes_CL
dc.subjectDeformable modelses_CL
dc.subjectDynamic PETes_CL
dc.subjectSegmentation 3Des_CL
dc.subjectModèles déformableses_CL
dc.subjectTEP dynamiquees_CL
dc.title4DGVF: Segmentation variationnelle pour images 3D multicomposanteses_CL
dc.title.alternative4DGVF: Variational segmentation of 3D vector-valued imageses_CL
dc.title.alternative4DGVF: Variational segmentation for multicomponent 3D Warehousees_CL
dc.ucm.facultadFacultad de Ciencias de la Ingenieríaes_CL

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Atribución-NoComercial-SinDerivadas 3.0 Chile
Except where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 3.0 Chile