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SLIC: a whole brain parcellation toolbox
Copyright (C) 2016 Jing Wang
The SLIC toolbox contains five whole brain parcellation approaches that
operates on resting-state fMRI data. Three of them are reproduced from the
Ncut-based approaches in (Craddock et al., 2012, HBM) and (Shen et al.,
2013, Neuroimage). The remaining two are the mean SLIC and two-level SLIC
approaches, which combine Ncut and SLIC to perform whole brain
parcellation. By running this demo, you could reproduce the major results
in our paper. See the Readme_plus file for further information. This
project is also shared on NITRC, https://www.nitrc.org/projects/slic/.
Usage:
1. Download the preprocessed fMRI data from NITRC, and then uncompress
the data.
https://www.nitrc.org/frs/?group_id=1030
2. Run main.m to play this demo. It takes about 10 hours on a server with
40 CPUs and 256 GB memory.
Changes:
1. Don't discard the eigenvectors corresponding to the trivial eigenvalues
(<10^-4) anymore because this step is not necessary.
2. Set the error tolerance to 1e-3 and set the maximum iteration number to
100 for iterations.
3. Store usefull information in sInfo.mat.
Related Codes:
SLIC, https://github.com/yuzhounh/SLIC
SLIC_2, https://github.com/yuzhounh/SLIC_2
Reference:
Jing Wang, Haixian Wang. A supervoxel-based method for groupwise whole
brain parcellation with resting-state fMRI data. Frontiers in Human
Neuroscience. DOI: 10.3389/fnhum.2016.00659
Contact information:
Jing Wang
2018-6-20 15:11:46