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¶
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.
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'
- Mandatory Inputs:
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) – 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
.- Optional Inputs:
args (a string) – Additional parameters to the command. Maps to a command-line argument:
%s
.bc_order_val (an integer) – 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) – Maximum number of iterations. Maps to a command-line argument:
-max_iter %s
. (Nipype default value:100
)min_iter (an integer) – 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
.- Outputs:
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.