TY - GEN
T1 - Brain white matter lesions classification in multiple sclerosis subjects for the prognosis of future disability
AU - Loizou, Christos P.
AU - Kyriacou, Efthyvoulos C.
AU - Seimenis, Ioannis
AU - Pantziaris, Marios
AU - Christodoulou, Christodoulos
AU - Pattichis, Constantinos S.
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
KW - MRI
KW - multiple sclerosis
KW - texture classification
UR - http://www.scopus.com/inward/record.url?scp=80055054080&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-23960-1_47
DO - 10.1007/978-3-642-23960-1_47
M3 - Conference contribution
AN - SCOPUS:80055054080
SN - 9783642239595
VL - 364 AICT
T3 - IFIP Advances in Information and Communication Technology
SP - 400
EP - 409
BT - Artificial Intelligence Applications and Innovations - 12th INNS EANN-SIG International Conference, EANN 2011 and 7th IFIP WG 12.5 International Conference, AIAI 2011, Proceedings
T2 - 7th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2011
Y2 - 15 September 2011 through 18 September 2011
ER -