Novel Morphological and Appearance Features for Predicting Physical Disability from MR Images in Multiple Sclerosis Patients

  • Jeremy KawaharaEmail author
  • Chris McIntosh
  • Roger Tam
  • Ghassan Hamarneh
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 17)


Physical disability in patients with multiple sclerosis is determined by functional ability and quantified with numerical scores. In vivo studies using magnetic resonance imaging (MRI) have found that these scores correlate with spinal cord atrophy (loss of tissue), where atrophy is commonly measured by spinal cord volume or cross-sectional area. However, this correlation is generally weak to moderate, and improved measures would strengthen the utility of imaging biomarkers. We propose novel spinal cord morphological and MRI-based appearance features. Select features are used to train regression models to predict patients’ physical disability scores. We validate our models using 30 MRI scans of different patients with varying levels of disability. Our results suggest that regression models trained with multiple spinal cord features predict clinical disability better than a model based on the volume of the spinal cord alone.


Spinal Cord Expand Disability Status Scale Clinical Score Expand Disability Status Scale Score Simple Linear Regression Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



JK, RT, and GH were partially supported by NSERC and Biogen Idec Canada. CM was supported by the Canadian Breast Cancer Foundation and the Canadian Cancer Society Research Institute.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jeremy Kawahara
    • 1
    Email author
  • Chris McIntosh
    • 1
    • 2
  • Roger Tam
    • 3
  • Ghassan Hamarneh
    • 1
  1. 1.Medical Image Analysis Lab.Simon Fraser UniversityBurnabyCanada
  2. 2.Princess Margaret Cancer CentreUniversity Health NetworkTorontoCanada
  3. 3.MS/MRI Research GroupUniversity of British ColumbiaVancouverCanada

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