@inproceedings{b86106ac84b54d9b81d5b6f9f4c689de,
title = "Voxel-wise displacement as independent features in classification of multiple sclerosis",
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%).",
keywords = "Classification, Image registration, Magnetic resonance imaging, Multiple sclerosis",
author = "Min Chen and Aaron Carass and Reich, {Daniel S.} and Calabresi, {Peter A.} and Dzung Pham and Prince, {Jerry L.}",
year = "2013",
doi = "10.1117/12.2007150",
language = "English (US)",
isbn = "9780819494436",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
booktitle = "Medical Imaging 2013",
note = "Medical Imaging 2013: Image Processing ; Conference date: 10-02-2013 Through 12-02-2013",
}