Publication: Positron emission tomography image segmentation based on atanassov's intuitionistic fuzzy sets
| dc.contributor.author | Couto, Pedro | |
| dc.contributor.author | Bento, Telmo | |
| dc.contributor.author | Bustince Sola, Humberto | |
| dc.contributor.author | Melo-Pinto, Pedro | |
| dc.contributor.department | Automática y Computación | es_ES |
| dc.contributor.department | Automatika eta Konputazioa | eu |
| dc.date.accessioned | 2022-08-04T10:50:32Z | |
| dc.date.available | 2022-08-04T10:50:32Z | |
| dc.date.issued | 2022 | |
| dc.date.updated | 2022-08-04T10:47:35Z | |
| dc.description.abstract | In this paper, we present an approach to fully automate tumor delineation in positron emission tomography (PET) images. PET images play a major role in medicine for in vivo imaging in oncology (PET images are used to evaluate oncology patients, detecting emitted photons from a radiotracer localized in abnormal cells). PET image tumor delineation plays a vital role both in pre-and post-treatment stages. The low spatial resolution and high noise characteristics of PET images increase the challenge in PET image segmentation. Despite the difficulties and known limitations, several image segmentation approaches have been proposed. This paper introduces a new unsupervised approach to perform tumor delineation in PET images using Atanassov's intuitionistic fuzzy sets (A-IFSs) and restricted dissimilarity functions. Moreover, the implementation of this methodology is presented and tested against other existing methodologies. The proposed algorithm increases the accuracy of tumor delineation in PET images, and the experimental results show that the proposed method outperformed all methods tested. | en |
| dc.description.sponsorship | This research was funded by by National Funds by FCT-Portuguese Foundation for Science and Technology, under the project UIDB/04033/2020. | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Couto, P.; Bento, T.; Bustince, H.; Melo-Pinto, P.. (2022). Positron emission tomography image segmentation based on atanassov's intuitionistic fuzzy sets. Applied Sicences. 12,10. | en |
| dc.identifier.doi | 10.3390/app12104865 | |
| dc.identifier.issn | 2076-3417 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/43698 | |
| dc.language.iso | eng | en |
| dc.publisher | MDPI | en |
| dc.relation.ispartof | Applied Sicences, 2022, 12 (10) | en |
| dc.relation.publisherversion | https://doi.org/10.3390/app12104865 | |
| dc.rights | © 2022 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license | en |
| dc.rights.accessRights | Acceso abierto / Sarbide irekia | es |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | en |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | AIFS-s | en |
| dc.subject | PET image segmentation | en |
| dc.subject | Tumor delineation | en |
| dc.title | Positron emission tomography image segmentation based on atanassov's intuitionistic fuzzy sets | en |
| dc.type | Artículo / Artikulua | es |
| dc.type | info:eu-repo/semantics/article | en |
| dc.type.version | Versión publicada / Argitaratu den bertsioa | es |
| dc.type.version | info:eu-repo/semantics/publishedVersion | en |
| dspace.entity.type | Publication | |
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| relation.isAuthorOfPublication.latestForDiscovery | 1bdd7a0e-704f-48e5-8d27-4486444f82c9 |