.. AUTO-GENERATED FILE -- DO NOT EDIT!

interfaces.ants.segmentation
============================


.. _nipype.interfaces.ants.segmentation.AntsJointFusion:


.. index:: AntsJointFusion

AntsJointFusion
---------------

`Link to code <file:///build/nipype-1.1.8/nipype/interfaces/ants/segmentation.py#L1291>`__

Wraps the executable command ``antsJointFusion``.

Examples
~~~~~~~~

>>> from nipype.interfaces.ants import AntsJointFusion
>>> antsjointfusion = AntsJointFusion()
>>> antsjointfusion.inputs.out_label_fusion = 'ants_fusion_label_output.nii'
>>> antsjointfusion.inputs.atlas_image = [ ['rc1s1.nii','rc1s2.nii'] ]
>>> antsjointfusion.inputs.atlas_segmentation_image = ['segmentation0.nii.gz']
>>> antsjointfusion.inputs.target_image = ['im1.nii']
>>> antsjointfusion.cmdline
"antsJointFusion -a 0.1 -g ['rc1s1.nii', 'rc1s2.nii'] -l segmentation0.nii.gz -b 2.0 -o ants_fusion_label_output.nii -s 3x3x3 -t ['im1.nii']"

>>> antsjointfusion.inputs.target_image = [ ['im1.nii', 'im2.nii'] ]
>>> antsjointfusion.cmdline
"antsJointFusion -a 0.1 -g ['rc1s1.nii', 'rc1s2.nii'] -l segmentation0.nii.gz -b 2.0 -o ants_fusion_label_output.nii -s 3x3x3 -t ['im1.nii', 'im2.nii']"

>>> antsjointfusion.inputs.atlas_image = [ ['rc1s1.nii','rc1s2.nii'],
...                                        ['rc2s1.nii','rc2s2.nii'] ]
>>> antsjointfusion.inputs.atlas_segmentation_image = ['segmentation0.nii.gz',
...                                                    'segmentation1.nii.gz']
>>> antsjointfusion.cmdline
"antsJointFusion -a 0.1 -g ['rc1s1.nii', 'rc1s2.nii'] -g ['rc2s1.nii', 'rc2s2.nii'] -l segmentation0.nii.gz -l segmentation1.nii.gz -b 2.0 -o ants_fusion_label_output.nii -s 3x3x3 -t ['im1.nii', 'im2.nii']"

>>> antsjointfusion.inputs.dimension = 3
>>> antsjointfusion.inputs.alpha = 0.5
>>> antsjointfusion.inputs.beta = 1.0
>>> antsjointfusion.inputs.patch_radius = [3,2,1]
>>> antsjointfusion.inputs.search_radius = [3]
>>> antsjointfusion.cmdline
"antsJointFusion -a 0.5 -g ['rc1s1.nii', 'rc1s2.nii'] -g ['rc2s1.nii', 'rc2s2.nii'] -l segmentation0.nii.gz -l segmentation1.nii.gz -b 1.0 -d 3 -o ants_fusion_label_output.nii -p 3x2x1 -s 3 -t ['im1.nii', 'im2.nii']"

>>> antsjointfusion.inputs.search_radius = ['mask.nii']
>>> antsjointfusion.inputs.verbose = True
>>> antsjointfusion.inputs.exclusion_image = ['roi01.nii', 'roi02.nii']
>>> antsjointfusion.inputs.exclusion_image_label = ['1','2']
>>> antsjointfusion.cmdline
"antsJointFusion -a 0.5 -g ['rc1s1.nii', 'rc1s2.nii'] -g ['rc2s1.nii', 'rc2s2.nii'] -l segmentation0.nii.gz -l segmentation1.nii.gz -b 1.0 -d 3 -e 1[roi01.nii] -e 2[roi02.nii] -o ants_fusion_label_output.nii -p 3x2x1 -s mask.nii -t ['im1.nii', 'im2.nii'] -v"

>>> antsjointfusion.inputs.out_label_fusion = 'ants_fusion_label_output.nii'
>>> antsjointfusion.inputs.out_intensity_fusion_name_format = 'ants_joint_fusion_intensity_%d.nii.gz'
>>> antsjointfusion.inputs.out_label_post_prob_name_format = 'ants_joint_fusion_posterior_%d.nii.gz'
>>> antsjointfusion.inputs.out_atlas_voting_weight_name_format = 'ants_joint_fusion_voting_weight_%d.nii.gz'
>>> antsjointfusion.cmdline
"antsJointFusion -a 0.5 -g ['rc1s1.nii', 'rc1s2.nii'] -g ['rc2s1.nii', 'rc2s2.nii'] -l segmentation0.nii.gz -l segmentation1.nii.gz -b 1.0 -d 3 -e 1[roi01.nii] -e 2[roi02.nii]  -o [ants_fusion_label_output.nii, ants_joint_fusion_intensity_%d.nii.gz, ants_joint_fusion_posterior_%d.nii.gz, ants_joint_fusion_voting_weight_%d.nii.gz] -p 3x2x1 -s mask.nii -t ['im1.nii', 'im2.nii'] -v"

Inputs::

        [Mandatory]
        target_image: (a list of items which are a list of items which are an
                  existing file name)
                The target image (or multimodal target images) assumed to be aligned
                to a common image domain.
                argument: ``-t %s``
        atlas_image: (a list of items which are a list of items which are an
                  existing file name)
                The atlas image (or multimodal atlas images) assumed to be aligned
                to a common image domain.
                argument: ``-g %s...``
        atlas_segmentation_image: (a list of items which are an existing file
                  name)
                The atlas segmentation images. For performing label fusion the
                number of specified segmentations should be identical to the number
                of atlas image sets.
                argument: ``-l %s...``

        [Optional]
        verbose: (a boolean)
                Verbose output.
                argument: ``-v``
        out_intensity_fusion_name_format: (a unicode string)
                Optional intensity fusion image file name format. (e.g.
                "antsJointFusionIntensity_%d.nii.gz")
        exclusion_image: (a list of items which are an existing file name)
                Specify an exclusion region for the given label.
        mask_image: (an existing file name)
                If a mask image is specified, fusion is only performed in the mask
                region.
                argument: ``-x %s``
        out_atlas_voting_weight_name_format: (a unicode string)
                Optional atlas voting weight image file name format.
                requires: out_label_fusion, out_intensity_fusion_name_format,
                  out_label_post_prob_name_format
        patch_radius: (a list of items which are a value of type 'int')
                Patch radius for similarity measures.Default: 2x2x2
                argument: ``-p %s``
        out_label_fusion: (a file name)
                The output label fusion image.
                argument: ``%s``
        args: (a unicode string)
                Additional parameters to the command
                argument: ``%s``
        beta: (a float, nipype default value: 2.0)
                Exponent for mapping intensity difference to the joint error.
                Default = 2.0
                argument: ``-b %s``
        out_label_post_prob_name_format: (a unicode string)
                Optional label posterior probability image file name format.
                requires: out_label_fusion, out_intensity_fusion_name_format
        alpha: (a float, nipype default value: 0.1)
                Regularization term added to matrix Mx for calculating the inverse.
                Default = 0.1
                argument: ``-a %s``
        retain_atlas_voting_images: (a boolean, nipype default value: False)
                Retain atlas voting images. Default = false
                argument: ``-f``
        num_threads: (an integer (int or long), nipype default value: 1)
                Number of ITK threads to use
        retain_label_posterior_images: (a boolean, nipype default value:
                  False)
                Retain label posterior probability images. Requires atlas
                segmentations to be specified. Default = false
                argument: ``-r``
                requires: atlas_segmentation_image
        exclusion_image_label: (a list of items which are a unicode string)
                Specify a label for the exclusion region.
                argument: ``-e %s``
                requires: exclusion_image
        patch_metric: (u'PC' or u'MSQ')
                Metric to be used in determining the most similar neighborhood
                patch. Options include Pearson's correlation (PC) and mean squares
                (MSQ). Default = PC (Pearson correlation).
                argument: ``-m %s``
        search_radius: (a list of from 1 to 3 items which are any value,
                  nipype default value: [3, 3, 3])
                Search radius for similarity measures. Default = 3x3x3. One can also
                specify an image where the value at the voxel specifies the
                isotropic search radius at that voxel.
                argument: ``-s %s``
        constrain_nonnegative: (a boolean, nipype default value: False)
                Constrain solution to non-negative weights.
                argument: ``-c``
        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
        dimension: (3 or 2 or 4)
                This option forces the image to be treated as a specified-
                dimensional image. If not specified, the program tries to infer the
                dimensionality from the input image.
                argument: ``-d %d``

Outputs::

        out_intensity_fusion_name_format: (a unicode string)
        out_label_fusion: (an existing file name)
        out_label_post_prob_name_format: (a unicode string)
        out_atlas_voting_weight_name_format: (a unicode string)

.. _nipype.interfaces.ants.segmentation.Atropos:


.. index:: Atropos

Atropos
-------

`Link to code <file:///build/nipype-1.1.8/nipype/interfaces/ants/segmentation.py#L67>`__

Wraps the executable command ``Atropos``.

A finite mixture modeling (FMM) segmentation approach with possibilities for
specifying prior constraints. These prior constraints include the specification
of a prior label image, prior probability images (one for each class), and/or an
MRF prior to enforce spatial smoothing of the labels. Similar algorithms include
FAST and SPM.

Examples
~~~~~~~~

>>> from nipype.interfaces.ants import Atropos
>>> at = Atropos()
>>> at.inputs.dimension = 3
>>> at.inputs.intensity_images = 'structural.nii'
>>> at.inputs.mask_image = 'mask.nii'
>>> at.inputs.initialization = 'PriorProbabilityImages'
>>> at.inputs.prior_probability_images = ['rc1s1.nii', 'rc1s2.nii']
>>> at.inputs.number_of_tissue_classes = 2
>>> at.inputs.prior_weighting = 0.8
>>> at.inputs.prior_probability_threshold = 0.0000001
>>> at.inputs.likelihood_model = 'Gaussian'
>>> at.inputs.mrf_smoothing_factor = 0.2
>>> at.inputs.mrf_radius = [1, 1, 1]
>>> at.inputs.icm_use_synchronous_update = True
>>> at.inputs.maximum_number_of_icm_terations = 1
>>> at.inputs.n_iterations = 5
>>> at.inputs.convergence_threshold = 0.000001
>>> at.inputs.posterior_formulation = 'Socrates'
>>> at.inputs.use_mixture_model_proportions = True
>>> at.inputs.save_posteriors = True
>>> at.cmdline
'Atropos --image-dimensionality 3 --icm [1,1] --initialization PriorProbabilityImages[2,priors/priorProbImages%02d.nii,0.8,1e-07] --intensity-image structural.nii --likelihood-model Gaussian --mask-image mask.nii --mrf [0.2,1x1x1] --convergence [5,1e-06] --output [structural_labeled.nii,POSTERIOR_%02d.nii.gz] --posterior-formulation Socrates[1] --use-random-seed 1'

Inputs::

        [Mandatory]
        number_of_tissue_classes: (an integer (int or long))
        intensity_images: (a list of items which are an existing file name)
                argument: ``--intensity-image %s...``
        initialization: (u'Random' or u'Otsu' or u'KMeans' or
                  u'PriorProbabilityImages' or u'PriorLabelImage')
                argument: ``%s``
                requires: number_of_tissue_classes
        mask_image: (an existing file name)
                argument: ``--mask-image %s``

        [Optional]
        icm_use_synchronous_update: (a boolean)
                argument: ``%s``
        prior_probability_images: (a list of items which are an existing file
                  name)
        prior_weighting: (a float)
        out_classified_image_name: (a file name)
                argument: ``%s``
        mrf_smoothing_factor: (a float)
                argument: ``%s``
        convergence_threshold: (a float)
                requires: n_iterations
        prior_probability_threshold: (a float)
                requires: prior_weighting
        save_posteriors: (a boolean)
        maximum_number_of_icm_terations: (an integer (int or long))
                requires: icm_use_synchronous_update
        use_mixture_model_proportions: (a boolean)
                requires: posterior_formulation
        mrf_radius: (a list of items which are an integer (int or long))
                requires: mrf_smoothing_factor
        args: (a unicode string)
                Additional parameters to the command
                argument: ``%s``
        likelihood_model: (a unicode string)
                argument: ``--likelihood-model %s``
        use_random_seed: (a boolean, nipype default value: True)
                use random seed value over constant
                argument: ``--use-random-seed %d``
        output_posteriors_name_template: (a unicode string, nipype default
                  value: POSTERIOR_%02d.nii.gz)
        num_threads: (an integer (int or long), nipype default value: 1)
                Number of ITK threads to use
        n_iterations: (an integer (int or long))
                argument: ``%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
        posterior_formulation: (a unicode string)
                argument: ``%s``
        dimension: (3 or 2 or 4, nipype default value: 3)
                image dimension (2, 3, or 4)
                argument: ``--image-dimensionality %d``

Outputs::

        posteriors: (a list of items which are a file name)
        classified_image: (an existing file name)

.. _nipype.interfaces.ants.segmentation.BrainExtraction:


.. index:: BrainExtraction

BrainExtraction
---------------

`Link to code <file:///build/nipype-1.1.8/nipype/interfaces/ants/segmentation.py#L800>`__

Wraps the executable command ``antsBrainExtraction.sh``.

Examples
~~~~~~~~
>>> from nipype.interfaces.ants.segmentation import BrainExtraction
>>> brainextraction = BrainExtraction()
>>> brainextraction.inputs.dimension = 3
>>> brainextraction.inputs.anatomical_image ='T1.nii.gz'
>>> brainextraction.inputs.brain_template = 'study_template.nii.gz'
>>> brainextraction.inputs.brain_probability_mask ='ProbabilityMaskOfStudyTemplate.nii.gz'
>>> brainextraction.cmdline
'antsBrainExtraction.sh -a T1.nii.gz -m ProbabilityMaskOfStudyTemplate.nii.gz -e study_template.nii.gz -d 3 -s nii.gz -o highres001_'

Inputs::

        [Mandatory]
        brain_template: (an existing file name)
                Anatomical template created using e.g. LPBA40 data set with
                buildtemplateparallel.sh in ANTs.
                argument: ``-e %s``
        brain_probability_mask: (an existing file name)
                Brain probability mask created using e.g. LPBA40 data set which have
                brain masks defined, and warped to anatomical template and averaged
                resulting in a probability image.
                argument: ``-m %s``
        anatomical_image: (an existing file name)
                Structural image, typically T1. If more than one anatomical image is
                specified, subsequently specified images are used during the
                segmentation process. However, only the first image is used in the
                registration of priors. Our suggestion would be to specify the T1 as
                the first image. Anatomical template created using e.g. LPBA40 data
                set with buildtemplateparallel.sh in ANTs.
                argument: ``-a %s``

        [Optional]
        extraction_registration_mask: (an existing file name)
                Mask (defined in the template space) used during registration for
                brain extraction. To limit the metric computation to a specific
                region.
                argument: ``-f %s``
        num_threads: (an integer (int or long), nipype default value: 1)
                Number of ITK threads to use
        use_floatingpoint_precision: (0 or 1)
                Use floating point precision in registrations (default = 0)
                argument: ``-q %d``
        keep_temporary_files: (an integer (int or long))
                Keep brain extraction/segmentation warps, etc (default = 0).
                argument: ``-k %d``
        args: (a unicode string)
                Additional parameters to the command
                argument: ``%s``
        out_prefix: (a unicode string, nipype default value: highres001_)
                Prefix that is prepended to all output files (default =
                highress001_)
                argument: ``-o %s``
        image_suffix: (a unicode string, nipype default value: nii.gz)
                any of standard ITK formats, nii.gz is default
                argument: ``-s %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
        debug: (a boolean)
                If > 0, runs a faster version of the script. Only for testing.
                Implies -u 0. Requires single thread computation for complete
                reproducibility.
                argument: ``-z 1``
        dimension: (3 or 2, nipype default value: 3)
                image dimension (2 or 3)
                argument: ``-d %d``
        use_random_seeding: (0 or 1)
                Use random number generated from system clock in Atropos (default =
                1)
                argument: ``-u %d``

Outputs::

        BrainExtractionPrior1Warp: (an existing file name)
        BrainExtractionInitialAffine: (an existing file name)
        BrainExtractionBrain: (an existing file name)
                brain extraction image
        BrainExtractionSegmentation: (an existing file name)
                segmentation mask with CSF, GM, and WM
        BrainExtractionInitialAffineFixed: (an existing file name)
        BrainExtractionInitialAffineMoving: (an existing file name)
        BrainExtractionWM: (an existing file name)
                segmenration mask with only white matter
        BrainExtractionPrior0GenericAffine: (an existing file name)
        BrainExtractionPrior1InverseWarp: (an existing file name)
        BrainExtractionTemplateLaplacian: (an existing file name)
        BrainExtractionTmp: (an existing file name)
        BrainExtractionCSF: (an existing file name)
                segmentation mask with only CSF
        N4Truncated0: (an existing file name)
        BrainExtractionMask: (an existing file name)
                brain extraction mask
        BrainExtractionPriorWarped: (an existing file name)
        BrainExtractionGM: (an existing file name)
                segmentation mask with only grey matter
        BrainExtractionLaplacian: (an existing file name)
        N4Corrected0: (an existing file name)
                N4 bias field corrected image

.. _nipype.interfaces.ants.segmentation.CorticalThickness:


.. index:: CorticalThickness

CorticalThickness
-----------------

`Link to code <file:///build/nipype-1.1.8/nipype/interfaces/ants/segmentation.py#L597>`__

Wraps the executable command ``antsCorticalThickness.sh``.

Examples
~~~~~~~~
>>> from nipype.interfaces.ants.segmentation import CorticalThickness
>>> corticalthickness = CorticalThickness()
>>> corticalthickness.inputs.dimension = 3
>>> corticalthickness.inputs.anatomical_image ='T1.nii.gz'
>>> corticalthickness.inputs.brain_template = 'study_template.nii.gz'
>>> corticalthickness.inputs.brain_probability_mask ='ProbabilityMaskOfStudyTemplate.nii.gz'
>>> corticalthickness.inputs.segmentation_priors = ['BrainSegmentationPrior01.nii.gz',
...                                                 'BrainSegmentationPrior02.nii.gz',
...                                                 'BrainSegmentationPrior03.nii.gz',
...                                                 'BrainSegmentationPrior04.nii.gz']
>>> corticalthickness.inputs.t1_registration_template = 'brain_study_template.nii.gz'
>>> corticalthickness.cmdline
'antsCorticalThickness.sh -a T1.nii.gz -m ProbabilityMaskOfStudyTemplate.nii.gz -e study_template.nii.gz -d 3 -s nii.gz -o antsCT_ -p nipype_priors/BrainSegmentationPrior%02d.nii.gz -t brain_study_template.nii.gz'

Inputs::

        [Mandatory]
        segmentation_priors: (a list of items which are an existing file
                  name)
                argument: ``-p %s``
        brain_template: (an existing file name)
                Anatomical *intensity* template (possibly created using a population
                data set with buildtemplateparallel.sh in ANTs). This template is
                *not* skull-stripped.
                argument: ``-e %s``
        t1_registration_template: (an existing file name)
                Anatomical *intensity* template (assumed to be skull-stripped). A
                common case would be where this would be the same template as
                specified in the -e option which is not skull stripped.
                argument: ``-t %s``
        brain_probability_mask: (an existing file name)
                brain probability mask in template space
                argument: ``-m %s``
        anatomical_image: (an existing file name)
                Structural *intensity* image, typically T1. If more than one
                anatomical image is specified, subsequently specified images are
                used during the segmentation process. However, only the first image
                is used in the registration of priors. Our suggestion would be to
                specify the T1 as the first image.
                argument: ``-a %s``

        [Optional]
        use_random_seeding: (0 or 1)
                Use random number generated from system clock in Atropos (default =
                1)
                argument: ``-u %d``
        segmentation_iterations: (an integer (int or long))
                N4 -> Atropos -> N4 iterations during segmentation (default = 3)
                argument: ``-n %d``
        prior_segmentation_weight: (a float)
                Atropos spatial prior *probability* weight for the segmentation
                argument: ``-w %f``
        max_iterations: (an integer (int or long))
                ANTS registration max iterations (default = 100x100x70x20)
                argument: ``-i %d``
        extraction_registration_mask: (an existing file name)
                Mask (defined in the template space) used during registration for
                brain extraction.
                argument: ``-f %s``
        keep_temporary_files: (an integer (int or long))
                Keep brain extraction/segmentation warps, etc (default = 0).
                argument: ``-k %d``
        image_suffix: (a unicode string, nipype default value: nii.gz)
                any of standard ITK formats, nii.gz is default
                argument: ``-s %s``
        label_propagation: (a unicode string)
                Incorporate a distance prior one the posterior formulation. Should
                be of the form 'label[lambda,boundaryProbability]' where label is a
                value of 1,2,3,... denoting label ID. The label probability for
                anything outside the current label = boundaryProbability * exp(
                -lambda * distanceFromBoundary ) Intuitively, smaller lambda values
                will increase the spatial capture range of the distance prior. To
                apply to all label values, simply omit specifying the label, i.e. -l
                [lambda,boundaryProbability].
                argument: ``-l %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
        cortical_label_image: (an existing file name)
                Cortical ROI labels to use as a prior for ATITH.
        posterior_formulation: (a unicode string)
                Atropos posterior formulation and whether or not to use mixture
                model proportions. e.g 'Socrates[1]' (default) or 'Aristotle[1]'.
                Choose the latter if you want use the distance priors (see also the
                -l option for label propagation control).
                argument: ``-b %s``
        use_floatingpoint_precision: (0 or 1)
                Use floating point precision in registrations (default = 0)
                argument: ``-j %d``
        args: (a unicode string)
                Additional parameters to the command
                argument: ``%s``
        out_prefix: (a unicode string, nipype default value: antsCT_)
                Prefix that is prepended to all output files (default = antsCT_)
                argument: ``-o %s``
        num_threads: (an integer (int or long), nipype default value: 1)
                Number of ITK threads to use
        quick_registration: (a boolean)
                If = 1, use antsRegistrationSyNQuick.sh as the basis for
                registration during brain extraction, brain segmentation, and
                (optional) normalization to a template. Otherwise use
                antsRegistrationSyN.sh (default = 0).
                argument: ``-q 1``
        debug: (a boolean)
                If > 0, runs a faster version of the script. Only for testing.
                Implies -u 0. Requires single thread computation for complete
                reproducibility.
                argument: ``-z 1``
        b_spline_smoothing: (a boolean)
                Use B-spline SyN for registrations and B-spline exponential mapping
                in DiReCT.
                argument: ``-v``
        dimension: (3 or 2, nipype default value: 3)
                image dimension (2 or 3)
                argument: ``-d %d``

Outputs::

        SubjectToTemplate0GenericAffine: (an existing file name)
                Template to subject inverse affine
        CorticalThickness: (an existing file name)
                cortical thickness file
        TemplateToSubject0Warp: (an existing file name)
                Template to subject warp
        BrainSegmentationN4: (an existing file name)
                N4 corrected image
        ExtractedBrainN4: (an existing file name)
                extracted brain from N4 image
        BrainSegmentation: (an existing file name)
                brain segmentaion image
        BrainExtractionMask: (an existing file name)
                brain extraction mask
        TemplateToSubject1GenericAffine: (an existing file name)
                Template to subject affine
        SubjectToTemplateLogJacobian: (an existing file name)
                Template to subject log jacobian
        SubjectToTemplate1Warp: (an existing file name)
                Template to subject inverse warp
        BrainVolumes: (an existing file name)
                Brain volumes as text
        CorticalThicknessNormedToTemplate: (an existing file name)
                Normalized cortical thickness
        BrainSegmentationPosteriors: (a list of items which are an existing
                  file name)
                Posterior probability images

.. _nipype.interfaces.ants.segmentation.DenoiseImage:


.. index:: DenoiseImage

DenoiseImage
------------

`Link to code <file:///build/nipype-1.1.8/nipype/interfaces/ants/segmentation.py#L1122>`__

Wraps the executable command ``DenoiseImage``.

Examples
~~~~~~~~
>>> import copy
>>> from nipype.interfaces.ants import DenoiseImage
>>> denoise = DenoiseImage()
>>> denoise.inputs.dimension = 3
>>> denoise.inputs.input_image = 'im1.nii'
>>> denoise.cmdline
'DenoiseImage -d 3 -i im1.nii -n Gaussian -o im1_noise_corrected.nii -s 1'

>>> denoise_2 = copy.deepcopy(denoise)
>>> denoise_2.inputs.output_image = 'output_corrected_image.nii.gz'
>>> denoise_2.inputs.noise_model = 'Rician'
>>> denoise_2.inputs.shrink_factor = 2
>>> denoise_2.cmdline
'DenoiseImage -d 3 -i im1.nii -n Rician -o output_corrected_image.nii.gz -s 2'

>>> denoise_3 = DenoiseImage()
>>> denoise_3.inputs.input_image = 'im1.nii'
>>> denoise_3.inputs.save_noise = True
>>> denoise_3.cmdline
'DenoiseImage -i im1.nii -n Gaussian -o [ im1_noise_corrected.nii, im1_noise.nii ] -s 1'

Inputs::

        [Mandatory]
        input_image: (an existing file name)
                A scalar image is expected as input for noise correction.
                argument: ``-i %s``
        save_noise: (a boolean, nipype default value: False)
                True if the estimated noise should be saved to file.
                mutually_exclusive: noise_image

        [Optional]
        num_threads: (an integer (int or long), nipype default value: 1)
                Number of ITK threads to use
        shrink_factor: (an integer (int or long), nipype default value: 1)
                Running noise correction on large images can be time consuming. To
                lessen computation time, the input image can be resampled. The
                shrink factor, specified as a single integer, describes this
                resampling. Shrink factor = 1 is the default.
                argument: ``-s %s``
        verbose: (a boolean)
                Verbose output.
                argument: ``-v``
        noise_model: (u'Gaussian' or u'Rician', nipype default value:
                  Gaussian)
                Employ a Rician or Gaussian noise model.
                argument: ``-n %s``
        args: (a unicode string)
                Additional parameters to the command
                argument: ``%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
        noise_image: (a file name)
                Filename for the estimated noise.
        dimension: (2 or 3 or 4)
                This option forces the image to be treated as a specified-
                dimensional image. If not specified, the program tries to infer the
                dimensionality from the input image.
                argument: ``-d %d``
        output_image: (a file name)
                The output consists of the noise corrected version of the input
                image.
                argument: ``-o %s``

Outputs::

        output_image: (an existing file name)
        noise_image: (a file name)

.. _nipype.interfaces.ants.segmentation.JointFusion:


.. index:: JointFusion

JointFusion
-----------

`Link to code <file:///build/nipype-1.1.8/nipype/interfaces/ants/segmentation.py#L1003>`__

Wraps the executable command ``jointfusion``.

Examples
~~~~~~~~

>>> from nipype.interfaces.ants import JointFusion
>>> at = JointFusion()
>>> at.inputs.dimension = 3
>>> at.inputs.modalities = 1
>>> at.inputs.method = 'Joint[0.1,2]'
>>> at.inputs.output_label_image ='fusion_labelimage_output.nii'
>>> at.inputs.warped_intensity_images = ['im1.nii',
...                                      'im2.nii',
...                                      'im3.nii']
>>> at.inputs.warped_label_images = ['segmentation0.nii.gz',
...                                  'segmentation1.nii.gz',
...                                  'segmentation1.nii.gz']
>>> at.inputs.target_image = 'T1.nii'
>>> at.cmdline
'jointfusion 3 1 -m Joint[0.1,2] -tg T1.nii -g im1.nii -g im2.nii -g im3.nii -l segmentation0.nii.gz -l segmentation1.nii.gz -l segmentation1.nii.gz fusion_labelimage_output.nii'

>>> at.inputs.method = 'Joint'
>>> at.inputs.alpha = 0.5
>>> at.inputs.beta = 1
>>> at.inputs.patch_radius = [3,2,1]
>>> at.inputs.search_radius = [1,2,3]
>>> at.cmdline
'jointfusion 3 1 -m Joint[0.5,1] -rp 3x2x1 -rs 1x2x3 -tg T1.nii -g im1.nii -g im2.nii -g im3.nii -l segmentation0.nii.gz -l segmentation1.nii.gz -l segmentation1.nii.gz fusion_labelimage_output.nii'

Inputs::

        [Mandatory]
        modalities: (an integer (int or long))
                Number of modalities or features
                argument: ``%d``, position: 1
        warped_intensity_images: (a list of items which are an existing file
                  name)
                Warped atlas images
                argument: ``-g %s...``
        target_image: (a list of items which are an existing file name)
                Target image(s)
                argument: ``-tg %s...``
        warped_label_images: (a list of items which are an existing file
                  name)
                Warped atlas segmentations
                argument: ``-l %s...``
        output_label_image: (a file name)
                Output fusion label map image
                argument: ``%s``, position: -1
        dimension: (3 or 2 or 4, nipype default value: 3)
                image dimension (2, 3, or 4)
                argument: ``%d``, position: 0

        [Optional]
        num_threads: (an integer (int or long), nipype default value: 1)
                Number of ITK threads to use
        args: (a unicode string)
                Additional parameters to the command
                argument: ``%s``
        atlas_group_id: (a list of items which are a value of type 'int')
                Assign a group ID for each atlas
                argument: ``-gp %d...``
        patch_radius: (a list of items which are a value of type 'int')
                Patch radius for similarity measures, scalar or vector. Default:
                2x2x2
                argument: ``-rp %s``
        search_radius: (a list of items which are a value of type 'int')
                Local search radius. Default: 3x3x3
                argument: ``-rs %s``
        beta: (an integer (int or long), nipype default value: 0)
                Exponent for mapping intensity difference to joint error
                requires: method
        exclusion_region: (an existing file name)
                Specify an exclusion region for the given label.
                argument: ``-x %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
        alpha: (a float, nipype default value: 0.0)
                Regularization term added to matrix Mx for inverse
                requires: method
        atlas_group_weights: (a list of items which are a value of type
                  'int')
                Assign the voting weights to each atlas group
                argument: ``-gpw %d...``
        method: (a unicode string, nipype default value: )
                Select voting method. Options: Joint (Joint Label Fusion). May be
                followed by optional parameters in brackets, e.g., -m Joint[0.1,2]
                argument: ``-m %s``

Outputs::

        output_label_image: (an existing file name)

.. _nipype.interfaces.ants.segmentation.KellyKapowski:


.. index:: KellyKapowski

KellyKapowski
-------------

`Link to code <file:///build/nipype-1.1.8/nipype/interfaces/ants/segmentation.py#L1563>`__

Wraps the executable command ``KellyKapowski``.

Nipype Interface to ANTs' KellyKapowski, also known as DiReCT.

DiReCT is a registration based estimate of cortical thickness. It was published
in S. R. Das, B. B. Avants, M. Grossman, and J. C. Gee, Registration based
cortical thickness measurement, Neuroimage 2009, 45:867--879.

Examples
~~~~~~~~
>>> from nipype.interfaces.ants.segmentation import KellyKapowski
>>> kk = KellyKapowski()
>>> kk.inputs.dimension = 3
>>> kk.inputs.segmentation_image = "segmentation0.nii.gz"
>>> kk.inputs.convergence = "[45,0.0,10]"
>>> kk.inputs.thickness_prior_estimate = 10
>>> kk.cmdline
'KellyKapowski --convergence "[45,0.0,10]" --output "[segmentation0_cortical_thickness.nii.gz,segmentation0_warped_white_matter.nii.gz]" --image-dimensionality 3 --gradient-step 0.025000 --maximum-number-of-invert-displacement-field-iterations 20 --number-of-integration-points 10 --segmentation-image "[segmentation0.nii.gz,2,3]" --smoothing-variance 1.000000 --smoothing-velocity-field-parameter 1.500000 --thickness-prior-estimate 10.000000'

Inputs::

        [Mandatory]
        segmentation_image: (an existing file name)
                A segmentation image must be supplied labeling the gray and white
                matters. Default values = 2 and 3, respectively.
                argument: ``--segmentation-image "%s"``

        [Optional]
        gradient_step: (a float, nipype default value: 0.025)
                Gradient step size for the optimization.
                argument: ``--gradient-step %f``
        num_threads: (an integer (int or long), nipype default value: 1)
                Number of ITK threads to use
        white_matter_prob_image: (an existing file name)
                In addition to the segmentation image, a white matter probability
                image can be used. If no such image is supplied, one is created
                using the segmentation image and a variance of 1.0 mm.
                argument: ``--white-matter-probability-image "%s"``
        warped_white_matter: (a file name)
                Filename for the warped white matter file.
        gray_matter_prob_image: (an existing file name)
                In addition to the segmentation image, a gray matter probability
                image can be used. If no such image is supplied, one is created
                using the segmentation image and a variance of 1.0 mm.
                argument: ``--gray-matter-probability-image "%s"``
        white_matter_label: (an integer (int or long), nipype default value:
                  3)
                The label value for the white matter label in the
                segmentation_image.
        number_integration_points: (an integer (int or long), nipype default
                  value: 10)
                Number of compositions of the diffeomorphism per iteration.
                argument: ``--number-of-integration-points %d``
        args: (a unicode string)
                Additional parameters to the command
                argument: ``%s``
        thickness_prior_estimate: (a float, nipype default value: 10)
                Provides a prior constraint on the final thickness measurement in
                mm.
                argument: ``--thickness-prior-estimate %f``
        gray_matter_label: (an integer (int or long), nipype default value:
                  2)
                The label value for the gray matter label in the segmentation_image.
        dimension: (3 or 2, nipype default value: 3)
                image dimension (2 or 3)
                argument: ``--image-dimensionality %d``
        thickness_prior_image: (an existing file name)
                An image containing spatially varying prior thickness values.
                argument: ``--thickness-prior-image "%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
        cortical_thickness: (a file name)
                Filename for the cortical thickness.
                argument: ``--output "%s"``
        convergence: (a unicode string, nipype default value: )
                Convergence is determined by fitting a line to the normalized energy
                profile of the last N iterations (where N is specified by the window
                size) and determining the slope which is then compared with the
                convergence threshold.
                argument: ``--convergence "%s"``
        smoothing_variance: (a float, nipype default value: 1.0)
                Defines the Gaussian smoothing of the hit and total images.
                argument: ``--smoothing-variance %f``
        max_invert_displacement_field_iters: (an integer (int or long),
                  nipype default value: 20)
                Maximum number of iterations for estimating the invertdisplacement
                field.
                argument: ``--maximum-number-of-invert-displacement-field-iterations
                %d``
        smoothing_velocity_field: (a float, nipype default value: 1.5)
                Defines the Gaussian smoothing of the velocity field (default =
                1.5). If the b-spline smoothing option is chosen, then this defines
                the isotropic mesh spacing for the smoothing spline (default = 15).
                argument: ``--smoothing-velocity-field-parameter %f``
        use_bspline_smoothing: (a boolean)
                Sets the option for B-spline smoothing of the velocity field.
                argument: ``--use-bspline-smoothing 1``

Outputs::

        cortical_thickness: (a file name)
                A thickness map defined in the segmented gray matter.
        warped_white_matter: (a file name)
                A warped white matter image.

References:
~~~~~~~~~~~
None

.. _nipype.interfaces.ants.segmentation.LaplacianThickness:


.. index:: LaplacianThickness

LaplacianThickness
------------------

`Link to code <file:///build/nipype-1.1.8/nipype/interfaces/ants/segmentation.py#L237>`__

Wraps the executable command ``LaplacianThickness``.

Calculates the cortical thickness from an anatomical image

Examples
~~~~~~~~

>>> from nipype.interfaces.ants import LaplacianThickness
>>> cort_thick = LaplacianThickness()
>>> cort_thick.inputs.input_wm = 'white_matter.nii.gz'
>>> cort_thick.inputs.input_gm = 'gray_matter.nii.gz'
>>> cort_thick.cmdline
'LaplacianThickness white_matter.nii.gz gray_matter.nii.gz white_matter_thickness.nii.gz'

>>> cort_thick.inputs.output_image = 'output_thickness.nii.gz'
>>> cort_thick.cmdline
'LaplacianThickness white_matter.nii.gz gray_matter.nii.gz output_thickness.nii.gz'

Inputs::

        [Mandatory]
        input_gm: (a file name)
                gray matter segmentation image
                argument: ``%s``, position: 2
        input_wm: (a file name)
                white matter segmentation image
                argument: ``%s``, position: 1

        [Optional]
        num_threads: (an integer (int or long), nipype default value: 1)
                Number of ITK threads to use
        output_image: (a file name)
                name of output file
                argument: ``%s``, position: 3
        smooth_param: (a float)
                Sigma of the Laplacian Recursive Image Filter (defaults to 1)
                argument: ``%f``, position: 4
        args: (a unicode string)
                Additional parameters to the command
                argument: ``%s``
        prior_thickness: (a float)
                Prior thickness (defaults to 500)
                argument: ``%f``, position: 5
        sulcus_prior: (a float)
                Positive floating point number for sulcus prior. Authors said that
                0.15 might be a reasonable value
                argument: ``%f``, position: 7
        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
        dT: (a float)
                Time delta used during integration (defaults to 0.01)
                argument: ``%f``, position: 6
        tolerance: (a float)
                Tolerance to reach during optimization (defaults to 0.001)
                argument: ``%f``, position: 8

Outputs::

        output_image: (an existing file name)
                Cortical thickness

.. _nipype.interfaces.ants.segmentation.N4BiasFieldCorrection:


.. index:: N4BiasFieldCorrection

N4BiasFieldCorrection
---------------------

`Link to code <file:///build/nipype-1.1.8/nipype/interfaces/ants/segmentation.py#L312>`__

Wraps the executable command ``N4BiasFieldCorrection``.

N4 is a variant of the popular N3 (nonparameteric nonuniform normalization)
retrospective bias correction algorithm. Based on the assumption that the
corruption of the low frequency bias field can be modeled as a convolution of
the intensity histogram by a Gaussian, the basic algorithmic protocol is to
iterate between deconvolving the intensity histogram by a Gaussian, remapping
the intensities, and then spatially smoothing this result by a B-spline modeling
of the bias field itself. The modifications from and improvements obtained over
the original N3 algorithm are described in [Tustison2010]_.

.. [Tustison2010] N. Tustison et al.,
  N4ITK: Improved N3 Bias Correction, IEEE Transactions on Medical Imaging,
  29(6):1310-1320, June 2010.

Examples
~~~~~~~~

>>> import copy
>>> from nipype.interfaces.ants import N4BiasFieldCorrection
>>> n4 = N4BiasFieldCorrection()
>>> n4.inputs.dimension = 3
>>> n4.inputs.input_image = 'structural.nii'
>>> n4.inputs.bspline_fitting_distance = 300
>>> n4.inputs.shrink_factor = 3
>>> n4.inputs.n_iterations = [50,50,30,20]
>>> n4.cmdline
'N4BiasFieldCorrection --bspline-fitting [ 300 ] -d 3 --input-image structural.nii --convergence [ 50x50x30x20 ] --output structural_corrected.nii --shrink-factor 3'

>>> n4_2 = copy.deepcopy(n4)
>>> n4_2.inputs.convergence_threshold = 1e-6
>>> n4_2.cmdline
'N4BiasFieldCorrection --bspline-fitting [ 300 ] -d 3 --input-image structural.nii --convergence [ 50x50x30x20, 1e-06 ] --output structural_corrected.nii --shrink-factor 3'

>>> n4_3 = copy.deepcopy(n4_2)
>>> n4_3.inputs.bspline_order = 5
>>> n4_3.cmdline
'N4BiasFieldCorrection --bspline-fitting [ 300, 5 ] -d 3 --input-image structural.nii --convergence [ 50x50x30x20, 1e-06 ] --output structural_corrected.nii --shrink-factor 3'

>>> n4_4 = N4BiasFieldCorrection()
>>> n4_4.inputs.input_image = 'structural.nii'
>>> n4_4.inputs.save_bias = True
>>> n4_4.inputs.dimension = 3
>>> n4_4.cmdline
'N4BiasFieldCorrection -d 3 --input-image structural.nii --output [ structural_corrected.nii, structural_bias.nii ]'

Inputs::

        [Mandatory]
        save_bias: (a boolean, nipype default value: False)
                True if the estimated bias should be saved to file.
                mutually_exclusive: bias_image
        input_image: (a file name)
                input for bias correction. Negative values or values close to zero
                should be processed prior to correction
                argument: ``--input-image %s``
        copy_header: (a boolean, nipype default value: False)
                copy headers of the original image into the output (corrected) file

        [Optional]
        num_threads: (an integer (int or long), nipype default value: 1)
                Number of ITK threads to use
        shrink_factor: (an integer (int or long))
                argument: ``--shrink-factor %d``
        mask_image: (a file name)
                image to specify region to perform final bias correction in
                argument: ``--mask-image %s``
        output_image: (a unicode string)
                output file name
                argument: ``--output %s``
        n_iterations: (a list of items which are an integer (int or long))
                argument: ``--convergence %s``
        convergence_threshold: (a float)
                requires: n_iterations
        args: (a unicode string)
                Additional parameters to the command
                argument: ``%s``
        bspline_fitting_distance: (a float)
                argument: ``--bspline-fitting %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
        bspline_order: (an integer (int or long))
                requires: bspline_fitting_distance
        weight_image: (a file name)
                image for relative weighting (e.g. probability map of the white
                matter) of voxels during the B-spline fitting.
                argument: ``--weight-image %s``
        bias_image: (a file name)
                Filename for the estimated bias.
        dimension: (3 or 2 or 4, nipype default value: 3)
                image dimension (2, 3 or 4)
                argument: ``-d %d``

Outputs::

        bias_image: (an existing file name)
                Estimated bias
        output_image: (an existing file name)
                Warped image
