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dc.contributor.authorLoizou, Christos P.en
dc.contributor.authorKyriacou, Efthyvoulos C.en
dc.contributor.authorSeimenis, Ioannisen
dc.contributor.authorPantzaris, Marios C.en
dc.contributor.authorChristodoulou, Chris C.en
dc.contributor.authorPattichis, Constantinos S.en
dc.creatorLoizou, Christos P.en
dc.creatorKyriacou, Efthyvoulos C.en
dc.creatorSeimenis, Ioannisen
dc.creatorPantzaris, Marios C.en
dc.creatorChristodoulou, Chris C.en
dc.creatorPattichis, Constantinos S.en
dc.description.abstractThis study investigates the application of classification methods for the prognosis of future disability on MRI-detectable brain white matter lesions in subjects diagnosed with clinical isolated syndrome (CIS) of multiple sclerosis (MS). For this purpose, MS lesions and normal appearing white matter (NAWM) from 30 symptomatic untreated MS subjects, as well as normal white matter (NWM) from 20 healthy volunteers, were manually segmented, by an experienced MS neurologist, on transverse T2-weighted images obtained from serial brain MR imaging scans. A support vector machines classifier (SVM) based on texture features was developed to classify MRI lesions detected at the onset of the disease into two classes, those belonging to patients with EDSS≤2 and EDSS>2 (expanded disability status scale (EDSS) that was measured at 24 months after the onset of the disease). The highest percentage of correct classification's score achieved was 77%. The findings of this study provide evidence that texture features of MRI-detectable brain white matter lesions may have an additional potential role in the clinical evaluation of MRI images in MS. However, a larger scale study is needed to establish the application of texture analysis in clinical practice. © 2011 IFIP International Federation for Information Processing.en
dc.source7th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2011en
dc.subjectFeature extractionen
dc.subjectArtificial intelligenceen
dc.subjectmultiple sclerosisen
dc.subjectMagnetic resonance imagingen
dc.subjectSupport vector machinesen
dc.subjectHealthy volunteersen
dc.subjectMRI Imageen
dc.subjectClassification methodsen
dc.subjectTexture featuresen
dc.subjectTexture analysisen
dc.subjectBrain MRen
dc.subjectClinical evaluationen
dc.subjectClinical practicesen
dc.subjecttexture classificationen
dc.subjectWhite matteren
dc.subjectWhite matter lesionsen
dc.titleBrain white matter lesions classification in multiple sclerosis subjects for the prognosis of future disabilityen
dc.description.volume364 AICTen
dc.description.issuePART 2en
dc.description.endingpage409 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied SciencesΤμήμα Πληροφορικής / Department of Computer Science
dc.description.notes<p>Sponsors: International Federation for Information Processing (IFIP)en
dc.description.notesInternational Neural Network Society (INNS)en
dc.description.notesAristotle University of Thessalonikien
dc.description.notesDemocritus University of Thraceen
dc.description.notesIonian University of Corfuen
dc.description.notesConference code: 87083en
dc.description.notesCited By :4</p>en
dc.source.abbreviationIFIP Advances in Information and Communication Technologyen
dc.contributor.orcidPattichis, Constantinos S. [0000-0003-1271-8151]
dc.contributor.orcidChristodoulou, Chris C. [0000-0001-9398-5256]
dc.contributor.orcidKyriacou, Efthyvoulos C. [0000-0002-4589-519X]
dc.contributor.orcidLoizou, Christos P. [0000-0003-1247-8573]
dc.contributor.orcidPantzaris, Marios C. [0000-0003-2937-384X]

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