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Brain White Matter Lesions Classification in Multiple Sclerosis Subjects for the Prognosis of Future Disability

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.
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Submitted on : Wednesday, August 2, 2017 - 4:22:05 PM
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Christos Loizou, Efthyvoulos Kyriacou, Ioannis Seimenis, Marios Pantziaris, Christodoulos Christodoulou, et al.. Brain White Matter Lesions Classification in Multiple Sclerosis Subjects for the Prognosis of Future Disability. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. pp.400-409, ⟨10.1007/978-3-642-23960-1_47⟩. ⟨hal-01571457⟩

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