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# 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_filea pathlike object or string representing an existing file

>   Input image to segment. Maps to a command-line argument: `-in %s` (position: 4).

> no_prioran integer

>   Number of classes to use without prior. Maps to a command-line argument: `-nopriors %s`. Mutually exclusive with inputs: `prior_4D`, `priors`.

> prior_4Da 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`.

> priorsa 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`.

> argsa string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> bc_order_valan integer

>   Polynomial order for the bias field. Maps to a command-line argument: `-bc_order %s`. (Nipype default value: `3`)

> bc_thresh_vala 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`)

> environa 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_filea pathlike object or string representing an existing file

>   Filename of the ROI for label fusion. Maps to a command-line argument: `-mask %s`.

> max_iteran integer

>   Maximum number of iterations. Maps to a command-line argument: `-max_iter %s`. (Nipype default value: `100`)

> min_iteran integer

>   Minimum number of iterations. Maps to a command-line argument: `-min_iter %s`. (Nipype default value: `0`)

> mrf_beta_vala float

>   Weight of the Markov Random Field. Maps to a command-line argument: `-mrf_beta %s`.

> out_bc_filea pathlike object or string representing a file

>   Output bias corrected image. Maps to a command-line argument: `-bc_out %s`.

> out_filea pathlike object or string representing a file

>   Output segmentation. Maps to a command-line argument: `-out %s`.

> out_outlier_filea pathlike object or string representing a file

>   Output outlierness image. Maps to a command-line argument: `-out_outlier %s`.

> outlier_vala 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_vala float

>   Amount of regularization over the diagonal of the covariance matrix [above 1]. Maps to a command-line argument: `-reg %s`.

> relax_priorsa 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_filea pathlike object or string representing a file

>   Output bias corrected image.

> out_filea pathlike object or string representing a file

>   Output segmentation.

> out_outlier_filea pathlike object or string representing a file

>   Output outlierness image.