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Artículo
Self-assessed Contrast-Maximizing Adaptive Region Growing
dc.creator | Sánchez Mendoza, Carlos | es |
dc.creator | Acha Piñero, Begoña | es |
dc.creator | Serrano Gotarredona, María del Carmen | es |
dc.creator | Gómez-Cía, Tomás | es |
dc.date.accessioned | 2021-12-20T16:54:31Z | |
dc.date.available | 2021-12-20T16:54:31Z | |
dc.date.issued | 2009 | |
dc.identifier.citation | Sánchez Mendoza, C., Acha Piñero, B., Serrano Gotarredona, M.d.C. y Gómez-Cía, T. (2009). Self-assessed Contrast-Maximizing Adaptive Region Growing. Lecture Notes in Computer Science, 652-663. | |
dc.identifier.issn | 0302-9743 | es |
dc.identifier.uri | https://hdl.handle.net/11441/128505 | |
dc.description.abstract | In the context of an experimental virtual-reality surgical planning software platform, we propose a fully self-assessed adaptive region growing segmentation algorithm. Our method successfully delineates main tissues relevant to head and neck reconstructive surgery, such as skin, fat, muscle/organs, and bone. We rely on a standardized and self-assessed region-based approach to deal with a great variety of imaging conditions with minimal user intervention, as only a single-seed selection stage is required. The detection of the optimal parameters is managed internally using a measure of the varying contrast of the growing regions. Validation based on synthetic images, as well as truly-delineated real CT volumes, is provided for the reader’s evaluation. | es |
dc.format | application/pdf | es |
dc.format.extent | 12 p. | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | Lecture Notes in Computer Science, 652-663. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | CT | es |
dc.subject | Segmentation | es |
dc.subject | Region-growing | es |
dc.subject | Seed | es |
dc.subject | Muscle | es |
dc.subject | Bone | es |
dc.subject | Fait | es |
dc.subject | Surgical planning | es |
dc.subject | Virtual reality | es |
dc.title | Self-assessed Contrast-Maximizing Adaptive Region Growing | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/acceptedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones | es |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/978-3-642-04697-1_61 | es |
dc.journaltitle | Lecture Notes in Computer Science | es |
dc.publication.initialPage | 652 | es |
dc.publication.endPage | 663 | es |
dc.identifier.sisius | 6524639 | es |
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10.1.1.604.4569.pdf | 764.8Kb | ![]() | Ver/ | |