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3D shape prior active contours for an automatic segmentation of a patient specific femur from a CT scan

2013-10Book part Open access
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datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.fosEngenharia e Tecnologia::Outras Engenharias e Tecnologias
datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.sdg03:Saúde de Qualidade
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
datacite.subject.sdg12:Produção e Consumo Sustentáveis
dc.contributor.authorAlmeida, D.
dc.contributor.authorFolgado, J.
dc.contributor.authorFernandes, P.R.
dc.contributor.authorRuben, Rui
dc.date.accessioned2025-07-01T13:54:08Z
dc.date.available2025-07-01T13:54:08Z
dc.date.issued2013-10
dc.descriptionComputational Vision and Medical Image Processing, IV - Proceedings of Eccomas Thematic Conference on Computational Vision and Medical Image Processing, VIPIMAGE 2013, Pages 271 - 276, 2014 4th Eccomas Thematic Conference on Computational Vision and Medical Image Processing, VIPIMAGE 2013, Funchal, 14 October 2013, through 16 October 2014 - Code 166569
dc.description.abstractThe following paper describes a novel approach to a medical image segmentation problem. The fully automated computational procedure receives as input images from CT scan exams of the human femur and returns a three dimensional representation of the bone. This patient specific iterative approach is based in 3D active contours without edges, implemented over a level set framework, on which the evolution of the contour depends on local image parameters which can easily be defined by the user but also on a priori information about the volume to segment. This joint approach will lead to an optimal solution convergence of the iterative method. The resulting point cloud can be an excellent starting point for a Finite Element mesh generation and analysis or the basis for a stereolitography for example.eng
dc.identifier.citationD. Almeida, J. Folgado, P.R. Fernandes & R.B. Ruben. 3D shape prior active contours for an automatic segmentation of a patient specific femur from a CT scan. (2013). In Computational Vision and Medical Image Processing IV: VIPIMAGE 2013 (1st ed.). CRC Press. https://doi.org/10.1201/b15810-52.
dc.identifier.doi10.1201/b15810-52
dc.identifier.isbn9780429227646
dc.identifier.urihttp://hdl.handle.net/10400.8/13485
dc.language.isoeng
dc.peerreviewedyes
dc.publisherCRC Press
dc.relation.hasversionhttps://www.taylorfrancis.com/search?contributorName=D.%20Almeida,%20J.%20Folgado,%20P.R.%20Fernandes%20&%20R.B.%20Ruben=&contributorRole=author&redirectFromPDP=true&context=ubx
dc.relation.ispartofComputational Vision and Medical Image Processing IV
dc.rights.uriN/A
dc.subjectBone
dc.subjectFinite element method
dc.subjectImage processing
dc.subjectImage segmentation
dc.subjectIterative methods
dc.subjectMedical computing
dc.subjectMedical image processing
dc.subjectMedical imaging
dc.subjectMesh generation
dc.title3D shape prior active contours for an automatic segmentation of a patient specific femur from a CT scaneng
dc.typebook part
dspace.entity.typePublication
oaire.citation.title4th Eccomas Thematic Conference on Computational Vision and Medical Image Processing, VIPIMAGE 2013
oaire.versionhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43
person.familyNameRuben
person.givenNameRui
person.identifier46426
person.identifier.ciencia-id0A14-A279-7C05
person.identifier.orcid0000-0002-5407-0579
person.identifier.ridM-1119-2014
person.identifier.scopus-author-id7103127401
relation.isAuthorOfPublicatione69b86a2-a7dc-433d-94cb-0f3196fdc670
relation.isAuthorOfPublication.latestForDiscoverye69b86a2-a7dc-433d-94cb-0f3196fdc670

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The following paper describes a novel approach to a medical image segmentation problem. The fully automated computational procedure receives as input images from CT scan exams of the human femur and returns a three dimensional representation of the bone. This patient specific iterative approach is based in 3D active contours without edges, implemented over a level set framework, on which the evolution of the contour depends on local image parameters which can easily be defined by the user but also on a priori information about the volume to segment. This joint approach will lead to an optimal solution convergence of the iterative method. The resulting point cloud can be an excellent starting point for a Finite Element mesh generation and analysis or the basis for a stereolitography for example.
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