Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/23649
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dc.contributor.authorLoizou, Christos P.-
dc.contributor.authorPattichis, Constantinos S.-
dc.contributor.authorSeimenis, Ioannis-
dc.contributor.authorEracleous, Eleni A.-
dc.contributor.authorSchizas, Christos N.-
dc.contributor.authorPantziaris, Marios-
dc.date.accessioned2021-11-16T07:51:17Z-
dc.date.available2021-11-16T07:51:17Z-
dc.date.issued2008-01-22-
dc.identifier.citation5th International Conference on Information Technology and Application in Biomedicine, 2008, 30-31 May, Shenzhen, Chinaen_US
dc.identifier.isbn978-1-4244-2255-5-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/23649-
dc.description.abstractIn this study the value of magnetic resonance image (MRI) shape and texture analysis was assessed in multiple sclerosis (MS) subjects, both in differentiating between normal and abnormal tissue and in assessing disease progression. Shape and texture analysis was carried out in normal and diseased lesions in transverse sections of T2-weighted magnetic resonance (MR) images acquired from 10 symptomatic untreated subjects with clinically isolated syndrome (CIS) scanned twice, with an interval of 6-12 months. All detected brain lesions were manually segmented by an experienced neurologist and confirmed by a neuro-radiologist, whilst different shape and texture features were extracted from the segmented lesions. The results showed that there was no significance difference between shape features of 0 and 6-12 months. For some texture features there was significance difference between normal tissue and MS lesions at 0 and 6-12 months and between MS lesions at 0 and 6-12 months (i.e contrast, difference variance, difference entropy, and other). Further research with more subjects is required for computing shape and texture features that may provide information for differentiating between normal tissue and MS lesions as well as for longitudinal monitoring of these lesions. In addition the proposed methodology can be used for the assessment of subjects at risk of developing future neurological events. The extracted shape and features can also offer additional information of undiagnosed lesions.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofInternational Conference on Information Technology and Application in Biomedicineen_US
dc.rights© IEEEen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectLesionsen_US
dc.subjectMultiple sclerosisen_US
dc.subjectMagnetic analysisen_US
dc.subjectMagnetic resonanceen_US
dc.subjectImage texture analysisen_US
dc.subjectFeature extractionen_US
dc.subjectMagnetic resonance imageen_US
dc.subjectBrain lesionsen_US
dc.titleQuantitative analysis of brain white matter lesions in multiple sclerosis subjects: Preliminary findingsen_US
dc.typeConference Papersen_US
dc.collaborationIntercollegeen_US
dc.collaborationUniversity of Cyprusen_US
dc.collaborationAyios Therissos Medical Diagnostic Centeren_US
dc.collaborationCyprus Institute of Neurology and Geneticsen_US
dc.subject.categoryMedical Engineeringen_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1109/ITAB.2008.4570645en_US
dc.identifier.scopus2-s2.0-51849160892-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/51849160892-
cut.common.academicyear2007-2008en_US
dc.identifier.spage58en_US
dc.identifier.epage61en_US
item.fulltextNo Fulltext-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.openairetypeconferenceObject-
item.languageiso639-1en-
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-
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation
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