dc.contributor.author | Loizou, Christos P. | en |
dc.contributor.author | Kyriacou, Efthyvoulos C. | en |
dc.contributor.author | Seimenis, Ioannis | en |
dc.contributor.author | Pantzaris, Marios C. | en |
dc.contributor.author | Christodoulou, Chris C. | en |
dc.contributor.author | Pattichis, Constantinos S. | en |
dc.creator | Loizou, Christos P. | en |
dc.creator | Kyriacou, Efthyvoulos C. | en |
dc.creator | Seimenis, Ioannis | en |
dc.creator | Pantzaris, Marios C. | en |
dc.creator | Christodoulou, Chris C. | en |
dc.creator | Pattichis, Constantinos S. | en |
dc.date.accessioned | 2019-11-13T10:41:02Z | |
dc.date.available | 2019-11-13T10:41:02Z | |
dc.date.issued | 2011 | |
dc.identifier.issn | 1868-4238 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/54417 | |
dc.description.abstract | This 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.source | 7th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2011 | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-80055054080&doi=10.1007%2f978-3-642-23960-1_47&partnerID=40&md5=2bce33a9c05a0f9144fc9d63f4fde9d6 | |
dc.subject | Feature extraction | en |
dc.subject | Artificial intelligence | en |
dc.subject | multiple sclerosis | en |
dc.subject | Diagnosis | en |
dc.subject | Magnetic resonance imaging | en |
dc.subject | MRI | en |
dc.subject | Support vector machines | en |
dc.subject | Textures | en |
dc.subject | Healthy volunteers | en |
dc.subject | MRI Image | en |
dc.subject | Classification methods | en |
dc.subject | Texture features | en |
dc.subject | Texture analysis | en |
dc.subject | Brain MR | en |
dc.subject | Clinical evaluation | en |
dc.subject | Clinical practices | en |
dc.subject | texture classification | en |
dc.subject | White matter | en |
dc.subject | White matter lesions | en |
dc.title | Brain white matter lesions classification in multiple sclerosis subjects for the prognosis of future disability | en |
dc.type | info:eu-repo/semantics/article | |
dc.identifier.doi | 10.1007/978-3-642-23960-1_47 | |
dc.description.volume | 364 AICT | en |
dc.description.issue | PART 2 | en |
dc.description.startingpage | 400 | |
dc.description.endingpage | 409 | |
dc.author.faculty | 002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Πληροφορικής / Department of Computer Science | |
dc.type.uhtype | Article | en |
dc.description.notes | <p>Sponsors: International Federation for Information Processing (IFIP) | en |
dc.description.notes | International Neural Network Society (INNS) | en |
dc.description.notes | Aristotle University of Thessaloniki | en |
dc.description.notes | Democritus University of Thrace | en |
dc.description.notes | Ionian University of Corfu | en |
dc.description.notes | Conference code: 87083 | en |
dc.description.notes | Cited By :4</p> | en |
dc.source.abbreviation | IFIP Advances in Information and Communication Technology | en |
dc.contributor.orcid | Pattichis, Constantinos S. [0000-0003-1271-8151] | |
dc.contributor.orcid | Christodoulou, Chris C. [0000-0001-9398-5256] | |
dc.contributor.orcid | Kyriacou, Efthyvoulos C. [0000-0002-4589-519X] | |
dc.contributor.orcid | Loizou, Christos P. [0000-0003-1247-8573] | |
dc.contributor.orcid | Pantzaris, Marios C. [0000-0003-2937-384X] | |
dc.gnosis.orcid | 0000-0003-1271-8151 | |
dc.gnosis.orcid | 0000-0001-9398-5256 | |
dc.gnosis.orcid | 0000-0002-4589-519X | |
dc.gnosis.orcid | 0000-0003-1247-8573 | |
dc.gnosis.orcid | 0000-0003-2937-384X | |