Home · Quickstart · Documentation · About · Nipy

# 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 newbytes or None or a
             newstr or None and with values which are a newbytes or None or a
             newstr or None, 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: (u'stream' or u'allatonce' or u'file' or u'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