dipy_cluster_streamlines

Synopsis

Cluster streamlines.

Algorithms used to cluster streamlines are QuickBundles, QuickBundlesX or FastStreamlineSearch.

Usage

dipy_cluster_streamlines [OPTIONS] streamline_files

Input Parameters

• streamline_files

  Path to the streamline files. This path may contain wildcards to process multiple inputs at once.

General Options

• --method

  Clustering method to use. Options are:

  • ‘quickbundles’: QuickBundles with a single distance threshold.

  • ‘qbx_and_merge’: QuickBundlesX with multi-level thresholds, then merge.

  • ‘faststreamlines’: QuickBundles for initial centroids followed by FastStreamlineSearch for streamline reassignment.

  (default: qbx_and_merge)

• --threshold

  Distance threshold (mm) used by ‘quickbundles’ and ‘faststreamlines’ methods. (default: 10.0)

• --thresholds

  Comma-separated distance thresholds (mm) used by the ‘qbx_and_merge’ method (e.g. ‘30,20,10’). (default: 30,20,10)

• --nb_pts

  Number of points to resample each streamline to before clustering. Used by ‘qbx_and_merge’. (default: 20)

• --min_cluster_size

  Minimum number of streamlines a cluster must contain to be kept. (default: 1)

• --select_randomly

  Randomly select a subset of streamlines before clustering. Used by ‘qbx_and_merge’. (default: None)

• --max_radius

  Maximum search radius (mm) used by the ‘faststreamlines’ method. (default: 10.0)

Output Options

• --out_dir

  Output directory. (default: current directory)

• --out_centroids

  Filename for the output cluster centroids tractogram. (default: centroids.trx)

• --out_cluster_labels

  Filename for the output cluster label array (.npy). (default: cluster_labels.npy)

References

Garyfallidis, E., M. Brett, B. Amirbekian, A. Rokem, S. Van Der Walt, M. Descoteaux, and I. Nimmo-Smith. Dipy, a library for the analysis of diffusion MRI data. Frontiers in Neuroinformatics, 1-18, 2014.