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Automatic Classification and Segmentation of Low-Grade Gliomas in Magnetic Resonance Imaging

2020Journal article Restricted access
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dc.contributor.authorBarbosa, Marta
dc.contributor.authorMoreira, Pedro
dc.contributor.authorRibeiro, Rogério
dc.contributor.authorCoelho, Luis
dc.date.accessioned2021-04-30T14:39:13Z
dc.date.embargo2100
dc.date.issued2020
dc.descriptionProceedings of the Tenth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2018)pt_PT
dc.description.abstractIn this article a new methodology is proposed to tackle the problem of automatic segmentation of low-grade gliomas. The possibility of knowing the limits of this type of tumor is crucial for effectively characterizing the neoplasm, enabling, in certain cases, to obtain useful information about how to treat the patient in a more effective way. Using a database of magnetic resonance images, containing several occurrences of this type of tumors, and through a carefully designed image processing pipeline, the purpose of this work is to accurately locate, isolate and thus facilitate the classification of the pathology. The proposed methodology, described in detail, was able to achieve an accuracy of 87.5% for a binary classification task. The quality of the identified regions had an accuracy of 81.6%. These are promising results that may point the effectiveness of the approach. The low contrast of the images, as a result of the acquisition process, and the detection of very small tumors are still challenges that bring motivation to further pursue additional results.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationBarbosa, M., Moreira, P., Ribeiro, R., Coelho, L., “Automatic Classification and Segmentation of Low-Grade Gliomas in Magnetic Resonance Imaging”. (2020) Advances in Intelligent Systems and Computing, 942, pp. 43-50. DOI: https://doi.org/10.1007/978-3-030-17065-3_5pt_PT
dc.identifier.doi10.1007/978-3-030-17065-3_5pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/17880
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringerpt_PT
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007%2F978-3-030-17065-3_5pt_PT
dc.subjectLow-grade gliomapt_PT
dc.subjectImage segmentationpt_PT
dc.subjectMRIpt_PT
dc.titleAutomatic Classification and Segmentation of Low-Grade Gliomas in Magnetic Resonance Imagingpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage50pt_PT
oaire.citation.startPage43pt_PT
oaire.citation.titleAdvances in Intelligent Systems and Computingpt_PT
oaire.citation.volume942pt_PT
person.familyNameCoelho
person.givenNameLuis
person.identifier721155
person.identifier.ciencia-id9B14-241F-3743
person.identifier.orcid0000-0002-5673-7306
person.identifier.ridC-9695-2015
person.identifier.scopus-author-id55027243400
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication5d1adee2-3e4c-4c07-a88e-0887653056dd
relation.isAuthorOfPublication.latestForDiscovery5d1adee2-3e4c-4c07-a88e-0887653056dd

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