Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/23017
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Loizou, Christos P. | - |
dc.contributor.author | Kyriacou, Efthyvoulos C. | - |
dc.contributor.author | Griffin, Maura B. | - |
dc.contributor.author | Nicolaides, Andrew N. | - |
dc.contributor.author | Pattichis, Constantinos S. | - |
dc.date.accessioned | 2021-09-09T11:56:19Z | - |
dc.date.available | 2021-09-09T11:56:19Z | - |
dc.date.issued | 2021-09 | - |
dc.identifier.citation | IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2021, vol. 68, no. 9, pp. 3017-3026 | en_US |
dc.identifier.issn | 15258955 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/23017 | - |
dc.description.abstract | Recent studies have suggested that textural characteristics of the intima-media complex (IMC) may be more useful than the intima-media thickness (IMT) in evaluating cardiovascular risk. The primary aim of our study was to investigate the association between texture features of the common carotid IMC and prevalent clinical cardiovascular disease (CVD). The secondary aim was to determine whether IMT and IMC texture features vary between the left and right carotid arteries. The study was performed on 2208 longitudinal-section ultrasound images of the left (L) and right (R) common carotid artery (CCA), acquired from 569 men and 535 women out of which 125 had clinical CVD. L and R sides of the IMC were intensity normalized and despeckled. The IMC was semiautomatically delineated for all images using a semiautomated segmentation system, and 61 different texture features were extracted. The corresponding IMT semiautomated measurements (mean±SD) of the L and R sides were 0.73±0.21 mm/0.69±0.19 mm for the normal population and 0.83±0.17 mm/0.79±0.18 mm for those with CVD. IMC texture features did not differ between the right- and left-hand sides. Several texture features were independent predictors of the presence of CVD. The multivariate logistic regression analysis combining age, IMT, and texture features produced a receiver operating characteristic curve with an area under the curve of 89%. A correct classification rate of 77% for separating the normal subject (NOR) versus CVD subjects was achieved using the support vector machine classifier with a combination of clinical features, IMT, and extracted texture features. Texture features provide additional information on the presence of clinical CVD, which is over and above that provided by conventional risk factors or IMT alone. The value of IMC texture features in the prediction of future cardiovascular events should be tested in prospective studies. | en_US |
dc.format | en_US | |
dc.language.iso | en | en_US |
dc.relation.ispartof | IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control | en_US |
dc.rights | © IEEE | en_US |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | B-mode | en_US |
dc.subject | Cardiovasculardisease (CVD) | en_US |
dc.subject | Left and right common carotid artery (CCA) | en_US |
dc.subject | Texture analysis | en_US |
dc.subject | Ultrasound imaging | en_US |
dc.title | Association of Intima-Media Texture With Prevalence of Clinical Cardiovascular Disease | en_US |
dc.type | Article | en_US |
dc.collaboration | Cyprus University of Technology | en_US |
dc.collaboration | Cyprus Cardiovascular Disease and Educational Research Trust | en_US |
dc.collaboration | University of Cyprus | en_US |
dc.subject.category | Computer and Information Sciences | en_US |
dc.journals | Subscription | en_US |
dc.country | Cyprus | en_US |
dc.subject.field | Natural Sciences | en_US |
dc.publication | Peer Reviewed | en_US |
dc.identifier.doi | 10.1109/TUFFC.2021.3081137 | en_US |
dc.identifier.pmid | 33999819 | - |
dc.identifier.scopus | 2-s2.0-85107192480 | - |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/85107192480 | - |
dc.relation.issue | 9 | en_US |
dc.relation.volume | 68 | en_US |
cut.common.academicyear | 2021-2022 | en_US |
dc.identifier.spage | 3017 | en_US |
dc.identifier.epage | 3026 | en_US |
item.openairetype | article | - |
item.cerifentitytype | Publications | - |
item.fulltext | No Fulltext | - |
item.grantfulltext | none | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.languageiso639-1 | en | - |
crisitem.author.dept | Department of Electrical Engineering, Computer Engineering and Informatics | - |
crisitem.author.dept | Department of Electrical Engineering, Computer Engineering and Informatics | - |
crisitem.author.faculty | Faculty of Engineering and Technology | - |
crisitem.author.faculty | Faculty of Engineering and Technology | - |
crisitem.author.orcid | 0000-0003-1247-8573 | - |
crisitem.author.orcid | 0000-0002-4589-519X | - |
crisitem.author.parentorg | Faculty of Engineering and Technology | - |
crisitem.author.parentorg | Faculty of Engineering and Technology | - |
crisitem.journal.journalissn | 1525-8955 | - |
crisitem.journal.publisher | IEEE | - |
Appears in Collections: | Άρθρα/Articles |
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