dipy_median_otsu

Synopsis

Workflow wrapping the median_otsu segmentation method.

Applies median_otsu segmentation on each file found by ‘globing’ input_files and saves the results in a directory specified by out_dir.

Usage

dipy_median_otsu [OPTIONS] input_files

Input Parameters

• input_files

  Path to the input volumes. This path may contain wildcards to process multiple inputs at once.

General Options

• --save_masked

  Save mask.

• --median_radius

  Radius (in voxels) of the applied median filter.

• --numpass

  Number of pass of the median filter.

• --autocrop

  If True, the masked input_volumes will also be cropped using the bounding box defined by the masked data. For example, if diffusion images are of 1x1x1 (mm^3) or higher resolution auto-cropping could reduce their size in memory and speed up some of the analysis.

• --vol_idx

  1D array representing indices of axis=-1 of a 4D input_volume. From the command line use something like ‘1,2,3-5,7’. This input is required for 4D volumes.

• --dilate

  number of iterations for binary dilation.

• --finalize_mask

  Whether to remove potential holes or islands. Useful for solving minor errors.

Output Options

• --out_dir

  Output directory. (default current directory)

• --out_mask

  Name of the mask volume to be saved.

• --out_masked

  Name of the masked volume to be saved.

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.