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. (default: False)

  • --median_radius

    Radius (in voxels) of the applied median filter. (default: 2)

  • --numpass

    Number of pass of the median filter. (default: 5)

  • --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. (default: False)

  • --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. (default: None)

  • --dilate

    number of iterations for binary dilation. (default: None)

  • --finalize_mask

    Whether to remove potential holes or islands. Useful for solving minor errors. (default: False)

Output Options#

  • --out_dir

    Output directory. (default: current directory)

  • --out_mask

    Name of the mask volume to be saved. (default: brain_mask.nii.gz)

  • --out_masked

    Name of the masked volume to be saved. (default: dwi_masked.nii.gz)

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.

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