Publication
A Quality of Recognition Case Study: Texture-based Segmentation and MRI Quality Assessment
| dc.contributor.author | Rodrigues, Rafael | |
| dc.contributor.author | Pinheiro, Antonio M. G. | |
| dc.date.accessioned | 2021-02-04T17:24:36Z | |
| dc.date.available | 2021-02-04T17:24:36Z | |
| dc.date.issued | 2019-11-18 | |
| dc.description.abstract | Muscle texture may be used as a descriptive feature for the segmentation of skeletal muscle in Magnetic Resonance Images (MRI). However, MRI acquisition is not always ideal and the texture richness might become compromised. Moreover, the research for the development of texture quality metrics, and particularly no-reference metrics, to be applied to the specific context of MRI is still in a very early stage. In this paper, a case study is established from a texture-based segmentation approach for skeletal muscle, which was tested in a thigh Dixon MRI database. Upon the obtained performance measures, the relation between objective image quality and the texture MRI richness is explored, considering a set of state-of-the-art no-reference image quality metrics. A discussion on the effectiveness of existing quality assessment methods in measuring MRI texture quality is carried out, based on Pearson and Spearman correlation outcomes. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.23919/EUSIPCO.2019.8902776 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.6/11096 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | EURASIP | pt_PT |
| dc.relation | Texture Analysis of Magnetic Resonance Images for Diagnosis and Monitoring of Neuromuscular Diseases | |
| dc.relation | Instituto de Telecomunicações | |
| dc.relation.publisherversion | https://www.eurasip.org/Proceedings/Eusipco/eusipco2019/Proceedings/papers/1570534133.pdf | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | pt_PT |
| dc.subject | Magnetic Resonance Imaging | pt_PT |
| dc.subject | Objective Quality Assessment | pt_PT |
| dc.subject | Quality of Recognition | pt_PT |
| dc.subject | MRI Segmentation | pt_PT |
| dc.title | A Quality of Recognition Case Study: Texture-based Segmentation and MRI Quality Assessment | pt_PT |
| dc.type | conference object | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Texture Analysis of Magnetic Resonance Images for Diagnosis and Monitoring of Neuromuscular Diseases | |
| oaire.awardTitle | Instituto de Telecomunicações | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/OE/SFRH%2FBD%2F130858%2F2017/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FEEA%2F50008%2F2019/PT | |
| oaire.citation.conferencePlace | A Coruña, Spain | pt_PT |
| oaire.citation.endPage | 5 | pt_PT |
| oaire.citation.startPage | 1 | pt_PT |
| oaire.citation.title | 2019 27th European Signal Processing Conference (EUSIPCO) | pt_PT |
| oaire.fundingStream | OE | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| person.familyName | Mendes Rodrigues | |
| person.familyName | Pinheiro | |
| person.givenName | Jorge Rafael | |
| person.givenName | Antonio | |
| person.identifier.ciencia-id | D112-43CA-98E0 | |
| person.identifier.ciencia-id | 2218-265E-17D2 | |
| person.identifier.orcid | 0000-0002-9481-9601 | |
| person.identifier.orcid | 0000-0002-5968-9901 | |
| person.identifier.rid | B-2723-2012 | |
| person.identifier.scopus-author-id | 8420644500 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | conferenceObject | pt_PT |
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