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Brain white matter lesion classification in multiple sclerosis subjects for the prognosis of future disability
- Source :
- Intelligent Decision Technologies, Intelligent Decis.Technol, IFIP Advances in Information and Communication Technology ISBN: 9783642239595, EANN/AIAI (2)
- Publication Year :
- 2013
-
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 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. © 2013-IOS Press and the authors. All rights reserved. 7 1 3 10 Cited By :4
- Subjects :
- medicine.medical_specialty
Computer science
Decision trees
Neuroimaging
02 engineering and technology
multiple sclerosis
030218 nuclear medicine & medical imaging
Image texture analysis
White matter lesions
Lesion
03 medical and health sciences
Probabilistic neural networks
0302 clinical medicine
Magnetic resonance imaging
Image texture
Brain White Matter
Artificial Intelligence
Classification models
Diagnosis
0202 electrical engineering, electronic engineering, information engineering
medicine
10. No inequality
Image segmentation
Expanded Disability Status Scale
Support vector machines
Multiple sclerosis
Mechanical Engineering
Textures
medicine.disease
Mr imaging
Human-Computer Interaction
Support vector machine
Significant differences
Classification methods
Engineering and Technology
020201 artificial intelligence & image processing
texture classification
Computer Vision and Pattern Recognition
Radiology
medicine.symptom
Software
030217 neurology & neurosurgery
Neural networks
MRI
Subjects
Details
- ISBN :
- 978-3-642-23959-5
- ISBNs :
- 9783642239595
- Database :
- OpenAIRE
- Journal :
- Intelligent Decision Technologies, Intelligent Decis.Technol, IFIP Advances in Information and Communication Technology ISBN: 9783642239595, EANN/AIAI (2)
- Accession number :
- edsair.doi.dedup.....e5bad4369cd791e6f4a8c80d74c7915e