Affiliations: [a] Department of Computer Science, Intercollege, Limassol, Cyprus | [b] Department of Computer Science and Engineering, Frederick University, Limassol, Cyprus | [c] Medical Diagnostic Centre “Ayios Therissos”, Nicosia, Cyprus | [d] Department of Medical Physics, Medical School, Democritus University of Thrace, Alexandroupolis, Greece | [e] Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus | [f] Department of Computer Science, University of Cyprus, Nicosia, Cyprus | [x] University of Central Greece | [y] Democritus University of Thrace, Greece | [z] Frederick University, Cyprus
Correspondence:
[*]
Corresponding author: Christos P. Loizou, Department of Computer Science, Intercollege, P. O. Box 51604, CY-3507, Limassol, Cyprus. E-mail: [email protected]
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). In order to achieve these we had collected MS lesions from 38 subjects, manually segmented by an experienced MS neurologist, on transverse T2-weighted images obtained from serial brain MR imaging scans. The patients have been divided into two groups, those belonging to patients with EDSS ⩽ 2 and those belonging to patients with EDSS > 2 (expanded disability status scale (EDSS)) that was measured at 24 months after the onset of the disease). Several image texture analysis features were extracted from the plaques. Using the Mann-Whitey rank sum test at p < 0.05 we had identified the features that could give acceptable significant difference. Based on these features three different classification models were investigated for predicting a subject's disability score (two class models, EDSS ⩽ 2 and EDSS > 2). These models were based on the Support Vector Machines (SVM), the Probabilistic Neural Networks (PNN), and the decision trees algorithm (C4.5). The highest percentage of correct classification's score achieved was 69% when using the SVM classifier. 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 MR images in MS.
