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

Min Chen; Aaron Carass; Daniel S. Reich; Peter A. Calabresi; Dzung Pham; Jerry L. Prince

doi:10.1117/12.2007150

13 March 2013 Voxel-wise displacement as independent features in classification of multiple sclerosis

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

Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 86690K (2013) https://doi.org/10.1117/12.2007150
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

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%).

Citation Download Citation

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); https://doi.org/10.1117/12.2007150

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