Brain White Matter Lesions Classification in Multiple Sclerosis Subjects for the Prognosis of Future Disability
Date Issued
September 2011
DOI
10.1007/978-3-642-23960-1_47
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.
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.