Show simple item record

dc.creatorMir-Fuentes, Arnaues_ES
dc.creatorMir Torres, Arnaues_ES
dc.creatorAntunes Dos Santos, Felipees_ES
dc.creatorFernández Fernández, Francisco Javieres_ES
dc.creatorLópez Molina, Carloses_ES
dc.date.accessioned2023-09-26T16:49:31Z
dc.date.available2023-09-26T16:49:31Z
dc.date.issued2022
dc.identifier.citationMir-Fuentes, A., Antunes-Santos, F., Fernandez, F. J., Lopez-Molina, C. (2022) A framework for active contour initialization with application to liver segmentation in MRI. En Ciucci, D., Couso, I., Medina, J., Slezak, D., Petturiti, D., Bouchon-Meunier, B., Jager R. R. (Eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2022 (pp. 259-271). Springer International Publishing. https://doi.org/10.1007/978-3-031-08974-9_21.en
dc.identifier.isbn978-3-031-08973-2
dc.identifier.urihttps://hdl.handle.net/2454/46404
dc.description.abstractObject segmentation is a prominent low-level task in image processing and computer vision. A technique of special relevance within segmentation algorithms is active contour modeling. An active contour is a closed contour on an image which can be evolved to progressively fit the silhouette of certain area or object. Active contours shall be initialized as a closed contour at some position of the image, further evolving to precisely fit to the silhouette of the object of interest. While the evolution of the contour has been deeply studied in literature [5, 11], the study of strategies to define the initial location of the contour is rather absent from it. Typically, such contour is created as a small closed curve around an inner position in the object. However, literature contains no general-purpose algorithms to determine those inner positions, or to quantify their fitness. In fact, such points are frequently set manually by human experts, hence turning the segmentation process into a semi-supervised one. In this work, we present a method to find inner points in relevant object using spatial-tonal fuzzy clustering. Our proposal intends to detect dominant clusters of bright pixels, which are further used to identify candidate points or regions around which active contours can be initialized.en
dc.description.sponsorshipThe authors gratefully acknowledge the financial support of the grants PID2019-108392GB-I00 funded by MCIN/AEI/10.13039/501100011033, as well as that by the Government of Navarra (PC082-083-084 EHGNA). A. Mir acknowledges the financial support of the grant PID2020-113870GB-I00 funded by MCIN/AEI/10.13039/ 501100011033/.en
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherSpringeren
dc.relation.ispartofCiucci, D., Couso, I., Medina, J., Slezak, D., Petturiti, D., Bouchon-Meunier, B., Jager R. R. (Eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2022. Pp. 259-271. Springer. 978-3-031-08973-2en
dc.rights© 2022 Springer Nature Switzerland AGen
dc.subjectActive contour modelen
dc.subjectCenteren
dc.subjectConnected componenten
dc.subjectHepatic steatosisen
dc.subjectImage segmentationen
dc.subjectMRI imageen
dc.subjectSpatial fuzzy c-meansen
dc.titleA framework for active contour initialization with application to liver segmentation in MRIen
dc.typeCapítulo de libro / Liburuen kapituluaes
dc.typeinfo:eu-repo/semantics/bookParten
dc.date.updated2023-09-26T08:39:15Z
dc.contributor.departmentEstadística, Informática y Matemáticases_ES
dc.contributor.departmentEstatistika, Informatika eta Matematikaeu
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.identifier.doi10.1007/978-3-031-08974-9_21
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108392GB-I00/ES/en
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113870GB-I00/ES/en
dc.relation.projectIDinfo:eu-repo/grantAgreement/Gobierno de Navarra//PC082-083-084 EHGNAen
dc.relation.publisherversionhttps://doi.org/10.1007/978-3-031-08974-9_21
dc.type.versionVersión aceptada / Onetsi den bertsioaes
dc.type.versioninfo:eu-repo/semantics/acceptedVersionen


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record


El Repositorio ha recibido la ayuda de la Fundación Española para la Ciencia y la Tecnología para la realización de actividades en el ámbito del fomento de la investigación científica de excelencia, en la Línea 2. Repositorios institucionales (convocatoria 2020-2021).
Logo MinisterioLogo Fecyt