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Proceedings Paper

Voxel-wise displacement as independent features in classification of multiple sclerosis
Author(s): Min Chen; Aaron Carass; Daniel S. Reich; Peter A. Calabresi; Dzung Pham; Jerry L. Prince
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Paper 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%).

Paper Details

Date Published: 13 March 2013
PDF: 6 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86690K (13 March 2013); doi: 10.1117/12.2007150
Show Author Affiliations
Min Chen, Johns Hopkins Univ. (United States)
National Institute of Neurological Disorders and Stroke (United States)
Aaron Carass, Johns Hopkins Univ. (United States)
Daniel S. Reich, National Institute of Neurological Disorders and Stroke (United States)
Peter A. Calabresi, Johns Hopkins Univ. School of Medicine (United States)
Dzung Pham, Ctr. for Neuroscience and Regenerative Medicine (United States)
Jerry L. Prince, Johns Hopkins Univ. (United States)


Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)

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