title: An Automated Method for Segmenting White Matter Lesions through Multi-Level Morphometric Feature Classification with Application to Lupus.
abstract: 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.
authors.affiliation: The Mind Research Network Albuquerque, NM, USA.
authors.forename: Mark
authors.initials: M
authors.lastname: Scully
authors.forename: Blake
authors.initials: B
authors.lastname: Anderson
authors.forename: Terran
authors.initials: T
authors.lastname: Lane
authors.forename: Charles
authors.initials: C
authors.lastname: Gasparovic
authors.forename: Vince
authors.initials: V
authors.lastname: Magnotta
authors.forename: Wilmer
authors.initials: W
authors.lastname: Sibbitt
authors.forename: Carlos
authors.initials: C
authors.lastname: Roldan
authors.forename: Ron
authors.initials: R
authors.lastname: Kikinis
authors.forename: Henry J
authors.initials: HJ
authors.lastname: Bockholt