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13 March 2013 Voxel-wise displacement as independent features in classification of multiple sclerosis
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Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 86690K (2013)
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States
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%).
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Min Chen, Aaron Carass, Daniel S. Reich, Peter A. Calabresi, Dzung Pham, and Jerry L. Prince "Voxel-wise displacement as independent features in classification of multiple sclerosis", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86690K (13 March 2013);

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