An Automated Method for Segmenting White Matter Lesions through Multi-Level Morphometric Feature Classification with Application to Lupus.
We demonstrate an automated, multi-level method to segment white matter brain lesions and apply it to lupus. The method makes use of local morphometric features based on multiple MR sequences, including T1-weighted, T2-weighted, and fluid attenuated inversion recovery. After preprocessing, including co-registration, brain extraction, bias correction, and intensity standardization, 49 features are calculated for each brain voxel based on local morphometry. At each level of segmentation a supervised classifier takes advantage of a different subset of the features to conservatively segment lesion voxels, passing on more difficult voxels to the next classifier. This multi-level approach allows for a fast lesion classification method with tunable trade-offs between sensitivity and specificity producing accuracy comparable to a human rater.
Scully
Mark
M
The Mind Research Network Albuquerque, NM, USA.
Anderson
Blake
B
Lane
Terran
T
Gasparovic
Charles
C
Magnotta
Vince
V
Sibbitt
Wilmer
W
Roldan
Carlos
C
Kikinis
Ron
R
Bockholt
Henry J
HJ