interfaces.niftyseg.em

EM

Link to code

Wraps command seg_EM

Interface for executable seg_EM from NiftySeg platform.

seg_EM is a general purpose intensity based image segmentation tool. In it’s simplest form, it takes in one 2D or 3D image and segments it in n classes.

Source code | Documentation

Examples

>>> from nipype.interfaces import niftyseg
>>> node = niftyseg.EM()
>>> node.inputs.in_file = 'im1.nii'
>>> node.inputs.no_prior = 4
>>> node.cmdline  
'seg_EM -in im1.nii -nopriors 4 -bc_out im1_bc_em.nii.gz -out im1_em.nii.gz -out_outlier im1_outlier_em.nii.gz'

Inputs:

[Mandatory]
in_file: (an existing file name)
        Input image to segment
        flag: -in %s, position: 4
no_prior: (an integer (int or long))
        Number of classes to use without prior
        flag: -nopriors %s
        mutually_exclusive: prior_4D, priors
prior_4D: (an existing file name)
        4D file containing the priors
        flag: -prior4D %s
        mutually_exclusive: no_prior, priors
priors: (a list of items which are any value)
        List of priors filepaths.
        flag: %s
        mutually_exclusive: no_prior, prior_4D

[Optional]
args: (a unicode string)
        Additional parameters to the command
        flag: %s
bc_order_val: (an integer (int or long))
        Polynomial order for the bias field
        flag: -bc_order %s
bc_thresh_val: (a float)
        Bias field correction will run only if the ratio of improvement is
        below bc_thresh. (default=0 [OFF])
        flag: -bc_thresh %s
environ: (a dictionary with keys which are a bytes or None or a value
         of class 'str' and with values which are a bytes or None or a value
         of class 'str', nipype default value: {})
        Environment variables
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the
        interface fails to run
mask_file: (an existing file name)
        Filename of the ROI for label fusion
        flag: -mask %s
max_iter: (an integer (int or long))
        Maximum number of iterations
        flag: -max_iter %s
min_iter: (an integer (int or long))
        Minimun number of iterations
        flag: -min_iter %s
mrf_beta_val: (a float)
        Weight of the Markov Random Field
        flag: -mrf_beta %s
out_bc_file: (a file name)
        Output bias corrected image
        flag: -bc_out %s
out_file: (a file name)
        Output segmentation
        flag: -out %s
out_outlier_file: (a file name)
        Output outlierness image
        flag: -out_outlier %s
outlier_val: (a tuple of the form: (a float, a float))
        Outlier detection as in (Van Leemput TMI 2003). <fl1> is the
        Mahalanobis threshold [recommended between 3 and 7] <fl2> is a
        convergence ratio below which the outlier detection is going to be
        done [recommended 0.01]
        flag: -outlier %s %s
reg_val: (a float)
        Amount of regularization over the diagonal of the covariance matrix
        [above 1]
        flag: -reg %s
relax_priors: (a tuple of the form: (a float, a float))
        Relax Priors [relaxation factor: 0<rf<1 (recommended=0.5), gaussian
        regularization: gstd>0 (recommended=2.0)] /only 3D/
        flag: -rf %s %s
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

Outputs:

out_bc_file: (a file name)
        Output bias corrected image
out_file: (a file name)
        Output segmentation
out_outlier_file: (a file name)
        Output outlierness image