Center for Biomedical Image Computing and Analytics
SBIA License
Yes
University of Pennsylvania
NITRC
CBICA: ODVBA
C++
Alexander Getka
ODVBA provides a mathematically rigorous framework for determining the optimal spatial smoothing of structural and functional images, prior to applying voxel-based group analysis. In order to determine the optimal smoothing kernel, a local discriminative analysis, restricted by appropriate nonnegativity constraints, is applied to a spatial neighborhood around each voxel, aiming to find the direction best highlights the difference between two groups in that neighborhood. Since each voxel belongs to a large number of such neighborhoods, each centered on one of its neighboring voxels, the group difference at each voxel is determined by a composition of all these optimal smoothing directions. Permutation tests are used to obtain the statistical significance of the resulting Optimally-Discriminative VBM (ODVBA) maps.
2017-2-20
3.0.0
CBICA: ODVBA
Clinical Neuroinformatics, MR, SBIA License, C++
http://www.nitrc.org/projects/odvba/
Alexander.Getka@pennmedicine.upenn.edu