Dear SPMer,
I'd like to announce new VBM toolboxes for SPM2 and SPM5 available for download at:
http://dbm.neuro.uni-jena.de/vbm
The VBM toolboxes are a collection of extensions to the segmentation algorithm of SPM2 and
SPM5 to provide voxel-based morphometry (VBM). Some files in the VBM2 toolbox were previously
distributed as VBM-tools (cg_create_template.m / cg_vbm_optimized.m) and were based on
modifications of code snippets of John Ashburner to implement the so-called optimized VBM
protocol first proposed by Good et al. (2001). These files were extended by a couple of files in the
toolbox to provide segmentation and statistical analysis of cross-sectional data as well as
longitudinal data. The latter allow to track intra-subject effects over time by acquiring more than
one scan per subject.
The key change to the SPM segmentation algorithms and the old VBM-tools is the application of a
Hidden Markov Random Field (HMRF) model. This model provides spatial constraints based on
neighbouring voxels of a 3×3x3 cube. The idea is to remove isolated voxels of one tissue class
which are unlikely to be member of this tissue type. This procedure also closes holes in a cluster
of connected voxels of one tissue type. In the resulting segmentation the noise level will be
minimized.
The VBM5 toolbox extends the core segmentation algorithm by the HMRF approach and some
other useful options. A very helpful option is that you can use previously estimated segmentations
to save segmentations using different voxel size, to save additional tissue classes, or to apply MRF
and clean-up steps.
I hope, some of you may find these tools useful. If you have any comments or questions, please
contact me.
Best regards,
Christian
____________________________________________________________________________
Christian Gaser, Ph.D.
Assistant Professor of Computational Neuroscience
Department of Psychiatry
Friedrich-Schiller-University of Jena
Philosophenweg 3, D-07743 Jena, Germany
Tel: ++49-3641-935805 Fax: ++49-3641-935280
e-mail: [log in to unmask]
http://dbm.neuro.uni-jena.de
|