nipype.interfaces.niftyseg.em module

Nipype interface for seg_EM.

The em module provides higher-level interfaces to some of the operations that can be performed with the seg_em command-line program.

Examples

See the docstrings of the individual classes for examples.

EM

Link to code

Bases: NiftySegCommand

Wrapped executable: 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 -bc_order 3 -bc_thresh 0 -max_iter 100 -min_iter 0 -nopriors 4 -bc_out im1_bc_em.nii.gz -out im1_em.nii.gz -out_outlier im1_outlier_em.nii.gz'
in_file : a pathlike object or string representing an existing file
Input image to segment. Maps to a command-line argument: -in %s (position: 4).
no_prior : an integer (int or long)
Number of classes to use without prior. Maps to a command-line argument: -nopriors %s. Mutually exclusive with inputs: prior_4D, priors.
prior_4D : a pathlike object or string representing an existing file
4D file containing the priors. Maps to a command-line argument: -prior4D %s. Mutually exclusive with inputs: no_prior, priors.
priors : a list of items which are any value
List of priors filepaths. Maps to a command-line argument: %s. Mutually exclusive with inputs: no_prior, prior_4D.
args : a unicode string
Additional parameters to the command. Maps to a command-line argument: %s.
bc_order_val : an integer (int or long)
Polynomial order for the bias field. Maps to a command-line argument: -bc_order %s. (Nipype default value: 3)
bc_thresh_val : a float
Bias field correction will run only if the ratio of improvement is below bc_thresh. (default=0 [OFF]). Maps to a command-line argument: -bc_thresh %s. (Nipype default value: 0)
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’
Environment variables. (Nipype default value: {})
mask_file : a pathlike object or string representing an existing file
Filename of the ROI for label fusion. Maps to a command-line argument: -mask %s.
max_iter : an integer (int or long)
Maximum number of iterations. Maps to a command-line argument: -max_iter %s. (Nipype default value: 100)
min_iter : an integer (int or long)
Minimum number of iterations. Maps to a command-line argument: -min_iter %s. (Nipype default value: 0)
mrf_beta_val : a float
Weight of the Markov Random Field. Maps to a command-line argument: -mrf_beta %s.
out_bc_file : a pathlike object or string representing a file
Output bias corrected image. Maps to a command-line argument: -bc_out %s.
out_file : a pathlike object or string representing a file
Output segmentation. Maps to a command-line argument: -out %s.
out_outlier_file : a pathlike object or string representing a file
Output outlierness image. Maps to a command-line argument: -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]. Maps to a command-line argument: -outlier %s %s.
reg_val : a float
Amount of regularization over the diagonal of the covariance matrix [above 1]. Maps to a command-line argument: -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/. Maps to a command-line argument: -rf %s %s.
out_bc_file : a pathlike object or string representing a file
Output bias corrected image.
out_file : a pathlike object or string representing a file
Output segmentation.
out_outlier_file : a pathlike object or string representing a file
Output outlierness image.