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dc.contributor.authorMolinari, F.en
dc.contributor.authorPattichis, Constantinos S.en
dc.contributor.authorZeng, G.en
dc.contributor.authorSaba, L.en
dc.contributor.authorAcharya, U. R.en
dc.contributor.authorSanfilippo, R.en
dc.contributor.authorNicolaïdes, Andrew N.en
dc.contributor.authorSuri, J. S.en
dc.creatorMolinari, F.en
dc.creatorPattichis, Constantinos S.en
dc.creatorZeng, G.en
dc.creatorSaba, L.en
dc.creatorAcharya, U. R.en
dc.creatorSanfilippo, R.en
dc.creatorNicolaïdes, Andrew N.en
dc.creatorSuri, J. S.en
dc.date.accessioned2019-11-13T10:41:20Z
dc.date.available2019-11-13T10:41:20Z
dc.date.issued2012
dc.identifier.issn1057-7149
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/54556
dc.description.abstractThe aim of this paper is to describe a novel and completely automated technique for carotid artery (CA) recognition, far (distal) wall segmentation, and intima-media thickness (IMT) measurement, which is a strong clinical tool for risk assessment for cardiovascular diseases. The architecture of completely automated multiresolution edge snapper (CAMES) consists of the following two stages: 1) automated CA recognition based on a combination of scale-space and statistical classification in a multiresolution framework and 2) automated segmentation of lumen-intima (LI) and media-adventitia (MA) interfaces for the far (distal) wall and IMT measurement. Our database of 365 B-mode longitudinal carotid images is taken from four different institutions covering different ethnic backgrounds. The ground-truth (GT) database was the average manual segmentation from three clinical experts. The mean distance ± standard deviation of CAMES with respect to GT profiles for LI and MA interfaces were 0.081 ± 0.099 and 0.082 ± 0.197 mm, respectively. The IMT measurement error between CAMES and GT was 0.078 ± 0.112 mm. CAMES was benchmarked against a previously developed automated technique based on an integrated approach using feature-based extraction and classifier (CALEX). Although CAMES underestimated the IMT value, it had shown a strong improvement in segmentation errors against CALEX for LI and MA interfaces by 8% and 42%, respectively. The overall IMT measurement bias for CAMES improved by 36% against CALEX. Finally, this paper demonstrated that the figure-of-merit of CAMES was 95.8% compared with 87.4% for CALEX. The combination of multiresolution CA recognition and far-wall segmentation led to an automated, low-complexity, real-time, and accurate technique for carotid IMT measurement. Validation on a multiethnic/multi-institutional data set demonstrated the robustness of the technique, which can constitute a clinically valid IMT measurement for assistance in atherosclerosis disease management. © 2011 IEEE.en
dc.sourceIEEE Transactions on Image Processingen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84857322542&doi=10.1109%2fTIP.2011.2169270&partnerID=40&md5=6a375fefa2e4132326e2d1ab7843a66e
dc.subjectmethodologyen
dc.subjectarticleen
dc.subjectAlgorithmsen
dc.subjecthumanen
dc.subjectHumansen
dc.subjectalgorithmen
dc.subjectAtherosclerosisen
dc.subjectechographyen
dc.subjectTunica Intimaen
dc.subjectvalidation studyen
dc.subjectAutomationen
dc.subjectLow-complexityen
dc.subjectDiseasesen
dc.subjectUltrasonic imagingen
dc.subjectCardio-vascular diseaseen
dc.subjectCarotid Arteriesen
dc.subjectData setsen
dc.subjectDatabase systemsen
dc.subjectDatabases, Factualen
dc.subjectStandard deviationen
dc.subjectCarotid arteryen
dc.subjectFirst-orderen
dc.subjectImage segmentationen
dc.subjectcomputer assisted diagnosisen
dc.subjectimage enhancementen
dc.subjectImage Interpretation, Computer-Assisteden
dc.subjectSocieties and institutionsen
dc.subjectintimaen
dc.subjectsegmentationen
dc.subjectIntima-media thicknessen
dc.subjectintima-media thickness (IMT)en
dc.subjectultrasound imagingen
dc.subjectManual segmentationen
dc.subjecttunica mediaen
dc.subjectAutomated techniquesen
dc.subjectIntegrated approachen
dc.subjectMulti-resolutionsen
dc.subjectAutomated segmentationen
dc.subjectClinical toolsen
dc.subjectDisease managementen
dc.subjectedge detectionen
dc.subjectfactual databaseen
dc.subjectFeature-baseden
dc.subjectfirst-order absolute momenten
dc.subjectfirst-order Gaussian derivativeen
dc.subjectMean distancesen
dc.subjectMeasurement biasen
dc.subjectScale-spaceen
dc.subjectSegmentation erroren
dc.subjectStatistical classificationen
dc.subjectTwo stageen
dc.titleCompletely automated multiresolution edge snapper-A new technique for an accurate carotid ultrasound IMT measurement: Clinical validation and benchmarking on a multi-institutional databaseen
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.1109/TIP.2011.2169270
dc.description.volume21
dc.description.issue3
dc.description.startingpage1211
dc.description.endingpage1222
dc.author.faculty002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences
dc.author.departmentΤμήμα Πληροφορικής / Department of Computer Science
dc.type.uhtypeArticleen
dc.description.notes<p>Cited By :37</p>en
dc.source.abbreviationIEEE Trans.Image Process.en
dc.contributor.orcidPattichis, Constantinos S. [0000-0003-1271-8151]
dc.gnosis.orcid0000-0003-1271-8151


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