Brain white matter lesion classification in multiple sclerosis subjects for the prognosis of future disability
Copyright policy )Brain white matter lesion classification in multiple sclerosis subjects for the prognosis of future disability
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 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.
Image segmentation, Support vector machines, Decision trees, Textures, Neuroimaging, multiple sclerosis, Image texture analysis, White matter lesions, Probabilistic neural networks, Magnetic resonance imaging, Significant differences, Classification models, Diagnosis, texture classification, Classification methods, Neural networks, MRI
Image segmentation, Support vector machines, Decision trees, Textures, Neuroimaging, multiple sclerosis, Image texture analysis, White matter lesions, Probabilistic neural networks, Magnetic resonance imaging, Significant differences, Classification models, Diagnosis, texture classification, Classification methods, Neural networks, MRI
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