Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/19259
DC FieldValueLanguage
dc.contributor.authorLoizou,  Christos P.-
dc.contributor.authorPantzaris, Marios C.-
dc.contributor.authorPattichis, Constantinos S.-
dc.date.accessioned2020-10-26T07:16:22Z-
dc.date.available2020-10-26T07:16:22Z-
dc.date.issued2020-11-
dc.identifier.citationMagnetic Resonance Imaging, 2020, vol. 73, pp. 192-202en_US
dc.identifier.issn0730725X-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/19259-
dc.description.abstractObjective: There is a clinical interest in identifying normal appearing white matter (NAWM) areas in brain T2-weighted (T2W) MRI scans in multiple sclerosis (MS) subjects. These areas are susceptible to disease development and areas need to be studied in order to find potential associations between texture feature changes and disease progression. Methods: The subjects investigated had a first demyelinating event (Clinically Isolated Syndrome-CIS) at baseline (Time0), and the NAWM0 (i.e. NAWM at Time0) of the brain tissue was subsequently converted to demyelinating plaques (as evaluated in a follow up MRI at Time6–12). 38 untreated subjects that had developed a CIS, had brain MRI scans within an interval of 6–12 months (Time6–12 at follow-up). An experienced MS neurologist manually delineated the demyelinating lesions at Time0 (L0) and at Time6–12 (L6–12). Areas in the Time6–12 MRI scans, where new lesions had been developed, were mapped back to their corresponding NAWM areas on the Time0 MR scans (ROIS0). In addition, contralateral ROIs of similar size and shape were segmented on the same images at Time0 (ROISC0) to form an intra-subject control group. Following that, texture features were extracted from all prescribed areas and MS lesions. Results: Texture features were used as input into Support Vector Machine (SVM) models to differentiate between the following: NAWM0 vs ROISC0, NAWM0 vs NAWM6–12, NAWM0 vs L0, NAWM6–12 vs L6–12, ROIS0 vs L0, ROIS0 vs L6–12 and ROIS0 vs ROISC0, where the corresponding % correct classifications scores were 89%, 95%, 98%, 92%, 85%, 90% and 65% respectively. Conclusions: Texture features may provide complementary information for following up the development and progression of MS disease. Future work will investigate the proposed method on more subjects.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofMagnetic Resonance Imagingen_US
dc.rights© Elsevieren_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectMRI imagingen_US
dc.subjectMultiple sclerosisen_US
dc.subjectContralateral lesion segmentationen_US
dc.subjectTexture analysisen_US
dc.subjectClassification analysisen_US
dc.titleNormal appearing brain white matter changes in relapsing multiple sclerosis: Texture image and classification analysis in serial MRI scansen_US
dc.typeArticleen_US
dc.collaborationCyprus University of Technologyen_US
dc.collaborationCyprus Institute of Neurology and Geneticsen_US
dc.collaborationUniversity of Cyprusen_US
dc.collaborationResearch Center on Interactive Media, Smart Systems and Emerging Technologiesen_US
dc.subject.categoryComputer and Information Sciencesen_US
dc.journalsSubscriptionen_US
dc.countryCyprusen_US
dc.subject.fieldNatural Sciencesen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1016/j.mri.2020.08.022en_US
dc.relation.volume73en_US
cut.common.academicyear2020-2021en_US
dc.identifier.spage192en_US
dc.identifier.epage202en_US
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.cerifentitytypePublications-
item.openairetypearticle-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0003-1247-8573-
crisitem.author.parentorgFaculty of Engineering and Technology-
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