Voxel-wise displacement as independent features in classification of multiple sclerosis

Min Chen, Aaron Carass, Daniel S. Reich, Peter Calabresi, Dzung Pham, Jerry Ladd Prince

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a method that utilizes registration displacement fields to perform accurate classification of magnetic resonance images (MRI) of the brain acquired from healthy individuals and patients diagnosed with multiple sclerosis (MS). Contrary to standard approaches, each voxel in the displacement field is treated as an independent feature that is classified individually. Results show that when used with a simple linear discriminant and majority voting, the approach is superior to using the displacement field with a single classifier, even when compared against more sophisticated classification methods such as adaptive boosting, random forests, and support vector machines. Leave-one-out cross-validation was used to evaluate this method for classifying images by disease, MS subtype (Acc: 77%{88%), and age (Acc: 96%{100%).

Original languageEnglish (US)
Title of host publicationProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume8669
DOIs
StatePublished - 2013
EventMedical Imaging 2013: Image Processing - Lake Buena Vista, FL, United States
Duration: Feb 10 2013Feb 12 2013

Other

OtherMedical Imaging 2013: Image Processing
CountryUnited States
CityLake Buena Vista, FL
Period2/10/132/12/13

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Keywords

  • Classification
  • Image registration
  • Magnetic resonance imaging
  • Multiple sclerosis

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

Cite this

Chen, M., Carass, A., Reich, D. S., Calabresi, P., Pham, D., & Prince, J. L. (2013). Voxel-wise displacement as independent features in classification of multiple sclerosis. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE (Vol. 8669). [86690K] https://doi.org/10.1117/12.2007150