dipy_median_otsu

Usage

dipy_median_otsu [-h] [–save_masked] [–median_radius int]

[–numpass int] [–autocrop] [–vol_idx [int [int …]]] [–dilate int] [–out_dir str] [–out_mask str] [–out_masked str] input_files

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.

Positional Arguments

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

Optional Arguments

-h, --help

show this help message and exit

--save_masked

Save mask.

--median_radius int

Radius (in voxels) of the applied median filter.

--numpass int

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 [int [int …]]

1D array representing indices of axis=-1 of a 4D input_volume. From the command line use something like 3 4 5 6. From script use something like [3, 4, 5, 6]. This input is required for 4D volumes.

--dilate int

number of iterations for binary dilation.

Output Arguments(Optional)

--out_dir str

Output directory. (default current directory)

--out_mask str

Name of the mask volume to be saved.

--out_masked str

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.