align
Bunch(**kwds)
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VerbosityLevels
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VerbosityLevels This enum defines the four levels of verbosity we use in the align module. |
Module: align._public
Registration API: simplified API for registration of MRI data and of
streamlines.
syn_registration(moving, static[, ...])
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Register a 2D/3D source image (moving) to a 2D/3D target image (static). |
register_dwi_to_template(dwi, gtab[, ...])
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Register DWI data to a template through the B0 volumes. |
write_mapping(mapping, fname)
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Write out a syn registration mapping to a nifti file. |
read_mapping(disp, domain_img, codomain_img)
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Read a syn registration mapping from a nifti file. |
resample(moving, static[, moving_affine, ...])
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Resample an image (moving) from one space to another (static). |
affine_registration(moving, static[, ...])
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Find the affine transformation between two 3D images. |
center_of_mass(moving, static[, ...])
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Implements a center of mass transform. |
translation(moving, static[, moving_affine, ...])
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Implements a translation transform. |
rigid(moving, static[, moving_affine, ...])
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Implements a rigid transform. |
rigid_isoscaling(moving, static[, ...])
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Implements a rigid isoscaling transform. |
rigid_scaling(moving, static[, ...])
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Implements a rigid scaling transform. |
affine(moving, static[, moving_affine, ...])
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Implements an affine transform. |
_METHOD_DICT
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dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2). |
register_series(series, ref[, pipeline, ...])
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Register a series to a reference image. |
register_dwi_series(data, gtab[, affine, ...])
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Register a DWI series to the mean of the B0 images in that series. |
motion_correction(data, gtab[, affine, ...])
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Apply a motion correction to a DWI dataset (Between-Volumes Motion correction) |
streamline_registration(moving, static[, ...])
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Register two collections of streamlines ('bundles') to each other. |
Module: align.cpd
Note
This file is copied (possibly with major modifications) from the
sources of the pycpd project - https://github.com/siavashk/pycpd.
It remains licensed as the rest of PyCPD (MIT license as of October 2010).
# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
#
# See COPYING file distributed along with the PyCPD package for the
# copyright and license terms.
#
# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
DeformableRegistration(X, Y[, sigma2, ...])
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Deformable point cloud registration. |
gaussian_kernel(X, beta[, Y])
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low_rank_eigen(G, num_eig)
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Calculate num_eig eigenvectors and eigenvalues of gaussian matrix G. |
initialize_sigma2(X, Y)
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Initialize the variance (sigma2). |
lowrankQS(G, beta, num_eig[, eig_fgt])
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Calculate eigenvectors and eigenvalues of gaussian matrix G. |
Module: align.imaffine
Affine image registration module consisting of the following classes:
- AffineMap: encapsulates the necessary information to perform affine
transforms between two domains, defined by a static and a moving
image. The domain of the transform is the set of points in the
static image’s grid, and the codomain is the set of points in
the moving image. When we call the transform method, AffineMap
maps each point x of the domain (static grid) to the codomain
(moving grid) and interpolates the moving image at that point
to obtain the intensity value to be placed at x in the resulting
grid. The transform_inverse method performs the opposite operation
mapping points in the codomain to points in the domain.
- ParzenJointHistogram: computes the marginal and joint distributions of
intensities of a pair of images, using Parzen windows [Parzen62]
with a cubic spline kernel, as proposed by Mattes et al. [Mattes03].
It also computes the gradient of the joint histogram w.r.t. the
parameters of a given transform.
- MutualInformationMetric: computes the value and gradient of the mutual
information metric the way Optimizer needs them. That is, given
a set of transform parameters, it will use ParzenJointHistogram
to compute the value and gradient of the joint intensity histogram
evaluated at the given parameters, and evaluate the the value and
gradient of the histogram’s mutual information.
- AffineRegistration: it runs the multi-resolution registration, putting
all the pieces together. It needs to create the scale space of the
images and run the multi-resolution registration by using the Metric
and the Optimizer at each level of the Gaussian pyramid. At each
level, it will setup the metric to compute value and gradient of the
metric with the input images with different levels of smoothing.
References
- [Parzen62] E. Parzen. On the estimation of a probability density
function and the mode. Annals of Mathematical Statistics,
33(3), 1065-1076, 1962.
- [Mattes03] Mattes, D., Haynor, D. R., Vesselle, H., Lewellen, T. K.,
& Eubank, W. PET-CT image registration in the chest using
free-form deformations. IEEE Transactions on Medical
Imaging, 22(1), 120-8, 2003.
AffineInversionError
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AffineInvalidValuesError
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AffineMap(affine[, domain_grid_shape, ...])
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MutualInformationMetric([nbins, ...])
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AffineRegistration([metric, level_iters, ...])
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_transform_method
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dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2). |
transform_centers_of_mass(static, ...)
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Transformation to align the center of mass of the input images. |
transform_geometric_centers(static, ...)
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Transformation to align the geometric center of the input images. |
transform_origins(static, static_grid2world, ...)
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Transformation to align the origins of the input images. |
Module: align.imwarp
Classes and functions for Symmetric Diffeomorphic Registration
Module: align.metrics
Metrics for Symmetric Diffeomorphic Registration
SimilarityMetric(dim)
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CCMetric(dim[, sigma_diff, radius])
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EMMetric(dim[, smooth, inner_iter, ...])
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SSDMetric(dim[, smooth, inner_iter, step_type])
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v_cycle_2d(n, k, delta_field, ...[, depth])
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Multi-resolution Gauss-Seidel solver using V-type cycles |
v_cycle_3d(n, k, delta_field, ...[, depth])
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Multi-resolution Gauss-Seidel solver using V-type cycles |
Module: align.reslice
reslice(data, affine, zooms, new_zooms[, ...])
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Reslice data with new voxel resolution defined by new_zooms. |
Module: align.streamlinear
StreamlineDistanceMetric([num_threads])
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BundleMinDistanceMetric([num_threads])
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Bundle-based Minimum Distance aka BMD |
BundleMinDistanceMatrixMetric([num_threads])
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Bundle-based Minimum Distance aka BMD |
BundleMinDistanceAsymmetricMetric([num_threads])
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Asymmetric Bundle-based Minimum distance. |
BundleSumDistanceMatrixMetric([num_threads])
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Bundle-based Sum Distance aka BMD |
JointBundleMinDistanceMetric([num_threads])
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Bundle-based Minimum Distance for joint optimization. |
StreamlineLinearRegistration([metric, x0, ...])
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StreamlineRegistrationMap(matopt, xopt, ...)
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JointStreamlineRegistrationMap(xopt, fopt, ...)
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logger
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Instances of the Logger class represent a single logging channel. |
bundle_sum_distance(t, static, moving[, ...])
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MDF distance optimization function (SUM). |
bundle_min_distance(t, static, moving)
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MDF-based pairwise distance optimization function (MIN). |
bundle_min_distance_fast(t, static, moving, ...)
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MDF-based pairwise distance optimization function (MIN). |
bundle_min_distance_asymmetric_fast(t, ...)
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MDF-based pairwise distance optimization function (MIN). |
remove_clusters_by_size(clusters[, min_size])
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progressive_slr(static, moving, metric, x0, ...)
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Progressive SLR. |
slr_with_qbx(static, moving[, x0, ...])
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Utility function for registering large tractograms. |
groupwise_slr(bundles[, x0, tol, max_iter, ...])
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Function to perform unbiased groupwise bundle registration. |
get_unique_pairs(n_bundle[, pairs])
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Make unique pairs from n_bundle bundles. |
compose_matrix44(t[, dtype])
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Compose a 4x4 transformation matrix. |
decompose_matrix44(mat[, size])
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Given a 4x4 homogeneous matrix return the parameter vector. |
Module: align.streamwarp
-
class dipy.align.Bunch(**kwds)
Bases: object
-
__init__(**kwds)
A ‘bunch’ of values (a replacement of Enum)
This is a temporary replacement of Enum, which is not available
on all versions of Python 2
VerbosityLevels
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dipy.align.VerbosityLevels()
VerbosityLevels
This enum defines the four levels of verbosity we use in the align
module.
NONE : do not print anything
STATUS : print information about the current status of the algorithm
DIAGNOSE : print high level information of the components involved in the
registration that can be used to detect a failing component.
DEBUG : print as much information as possible to isolate the cause of a bug.
syn_registration
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dipy.align._public.syn_registration(moving, static, moving_affine=None, static_affine=None, step_length=0.25, metric='CC', dim=3, level_iters=None, prealign=None, **metric_kwargs)
Register a 2D/3D source image (moving) to a 2D/3D target image (static).
Parameters
- moving, staticarray or nib.Nifti1Image or str.
Either as a 2D/3D array or as a nifti image object, or as
a string containing the full path to a nifti file.
- moving_affine, static_affine4x4 array, optional.
Must be provided for data provided as an array. If provided together
with Nifti1Image or str data, this input will over-ride the affine
that is stored in the data input. Default: use the affine stored
in data.
- metricstring, optional
The metric to be optimized. One of CC, EM, SSD,
Default: ‘CC’ => CCMetric.
- dim: int (either 2 or 3), optional
The dimensions of the image domain. Default: 3
- level_iterslist of int, optional
the number of iterations at each level of the Gaussian Pyramid (the
length of the list defines the number of pyramid levels to be
used). Default: [10, 10, 5].
- metric_kwargsdict, optional
Parameters for initialization of the metric object. If not provided,
uses the default settings of each metric.
Returns
- warped_movingndarray
The data in moving, warped towards the static data.
- forwardndarray (…, 3)
The vector field describing the forward warping from the source to the
target.
- backwardndarray (…, 3)
The vector field describing the backward warping from the target to the
source.
register_dwi_to_template
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dipy.align._public.register_dwi_to_template(dwi, gtab, dwi_affine=None, template=None, template_affine=None, reg_method='syn', **reg_kwargs)
Register DWI data to a template through the B0 volumes.
Parameters
- dwi4D array, nifti image or str
Containing the DWI data, or full path to a nifti file with DWI.
- gtabGradientTable or sequence of strings
The gradients associated with the DWI data, or a sequence with
(fbval, fbvec), full paths to bvals and bvecs files.
- dwi_affine4x4 array, optional
An affine transformation associated with the DWI. Required if data
is provided as an array. If provided together with nifti/path,
will over-ride the affine that is in the nifti.
- template3D array, nifti image or str
Containing the data for the template, or full path to a nifti file
with the template data.
- template_affine4x4 array, optional
An affine transformation associated with the template. Required if data
is provided as an array. If provided together with nifti/path,
will over-ride the affine that is in the nifti.
- reg_methodstr,
One of “syn” or “aff”, which designates which registration method is
used. Either syn, which uses the syn_registration() function
or affine_registration() function. Default: “syn”.
- reg_kwargskey-word arguments for
syn_registration() or affine_registration()
Returns
warped_b0, mapping: The fist is an array with the b0 volume warped to the
template. If reg_method is “syn”, the second is a DiffeomorphicMap class
instance that can be used to transform between the two spaces. Otherwise,
if reg_method is “aff”, this is a 4x4 matrix encoding the affine transform.
Notes
This function assumes that the DWI data is already internally registered.
See register_dwi_series().
write_mapping
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dipy.align._public.write_mapping(mapping, fname)
Write out a syn registration mapping to a nifti file.
Parameters
mapping : a DiffeomorphicMap object derived from syn_registration()
fname : str
Full path to the nifti file storing the mapping
Notes
The data in the file is organized with shape (X, Y, Z, 3, 2), such
that the forward mapping in each voxel is in data[i, j, k, :, 0] and
the backward mapping in each voxel is in data[i, j, k, :, 1].
read_mapping
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dipy.align._public.read_mapping(disp, domain_img, codomain_img, prealign=None)
Read a syn registration mapping from a nifti file.
Parameters
- dispstr or Nifti1Image
A file of image containing the mapping displacement field in each voxel
Shape (x, y, z, 3, 2)
domain_img : str or Nifti1Image
codomain_img : str or Nifti1Image
Returns
A DiffeomorphicMap object.
resample
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dipy.align._public.resample(moving, static, moving_affine=None, static_affine=None, between_affine=None)
Resample an image (moving) from one space to another (static).
Parameters
- movingarray, nifti image or str
Containing the data for the moving object, or full path to a nifti file
with the moving data.
- moving_affine4x4 array, optional
An affine transformation associated with the moving object. Required if
data is provided as an array. If provided together with nifti/path,
will over-ride the affine that is in the nifti.
- staticarray, nifti image or str
Containing the data for the static object, or full path to a nifti file
with the moving data.
- static_affine4x4 array, optional
An affine transformation associated with the static object. Required if
data is provided as an array. If provided together with nifti/path,
will over-ride the affine that is in the nifti.
- between_affine: 4x4 array, optional
If an additional affine is needed between the two spaces.
Default: identity (no additional registration).
Returns
A Nifti1Image class instance with the data from the moving object
resampled into the space of the static object.
affine_registration
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dipy.align._public.affine_registration(moving, static, moving_affine=None, static_affine=None, pipeline=None, starting_affine=None, metric='MI', level_iters=None, sigmas=None, factors=None, ret_metric=False, moving_mask=None, static_mask=None, **metric_kwargs)
Find the affine transformation between two 3D images. Alternatively, find
the combination of several linear transformations.
Parameters
- movingarray, nifti image or str
Containing the data for the moving object, or full path to a nifti file
with the moving data.
- staticarray, nifti image or str
Containing the data for the static object, or full path to a nifti file
with the moving data.
- moving_affine4x4 array, optional
An affine transformation associated with the moving object. Required if
data is provided as an array. If provided together with nifti/path,
will over-ride the affine that is in the nifti.
- static_affine4x4 array, optional
An affine transformation associated with the static object. Required if
data is provided as an array. If provided together with nifti/path,
will over-ride the affine that is in the nifti.
- pipelinelist of str, optional
Sequence of transforms to use in the gradual fitting. Default: gradual
fit of the full affine (executed from left to right):
["center_of_mass", "translation", "rigid", "affine"]
Alternatively, any other combination of the following registration
methods might be used: center_of_mass, translation, rigid,
rigid_isoscaling, rigid_scaling and affine.
- starting_affine: 4x4 array, optional
Initial guess for the transformation between the spaces.
Default: identity.
- metricstr, optional.
Currently only supports ‘MI’ for MutualInformationMetric.
- level_iterssequence, optional
AffineRegistration key-word argument: the number of iterations at each
scale of the scale space. level_iters[0] corresponds to the coarsest
scale, level_iters[-1] the finest, where n is the length of the
sequence. By default, a 3-level scale space with iterations
sequence equal to [10000, 1000, 100] will be used.
- sigmassequence of floats, optional
AffineRegistration key-word argument: custom smoothing parameter to
build the scale space (one parameter for each scale). By default,
the sequence of sigmas will be [3, 1, 0].
- factorssequence of floats, optional
AffineRegistration key-word argument: custom scale factors to build the
scale space (one factor for each scale). By default, the sequence of
factors will be [4, 2, 1].
- ret_metricboolean, optional
Set it to True to return the value of the optimized coefficients and
the optimization quality metric.
- moving_maskarray, shape (S’, R’, C’) or (R’, C’), optional
moving image mask that defines which pixels in the moving image
are used to calculate the mutual information.
- static_maskarray, shape (S, R, C) or (R, C), optional
static image mask that defines which pixels in the static image
are used to calculate the mutual information.
- nbinsint, optional
MutualInformationMetric key-word argument: the number of bins to be
used for computing the intensity histograms. The default is 32.
- sampling_proportionNone or float in interval (0, 1], optional
MutualInformationMetric key-word argument: There are two types of
sampling: dense and sparse. Dense sampling uses all voxels for
estimating the (joint and marginal) intensity histograms, while
sparse sampling uses a subset of them. If sampling_proportion is
None, then dense sampling is used. If sampling_proportion is a
floating point value in (0,1] then sparse sampling is used,
where sampling_proportion specifies the proportion of voxels to
be used. The default is None (dense sampling).
Returns
transformed : array with moving data resampled to the static space
after computing the affine transformation
affine : the affine 4x4 associated with the transformation.
xopt : the value of the optimized coefficients.
fopt : the value of the optimization quality metric.
Notes
Performs a gradual registration between the two inputs, using a pipeline
that gradually approximates the final registration. If the final default
step (affine) is omitted, the resulting affine may not have all 12
degrees of freedom adjusted.
center_of_mass
-
dipy.align._public.center_of_mass(moving, static, moving_affine=None, static_affine=None, *, pipeline=['center_of_mass'], starting_affine=None, metric='MI', level_iters=None, sigmas=None, factors=None, ret_metric=False, moving_mask=None, static_mask=None, **metric_kwargs)
Implements a center of mass transform. Based on affine_registration().
translation
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dipy.align._public.translation(moving, static, moving_affine=None, static_affine=None, *, pipeline=['translation'], starting_affine=None, metric='MI', level_iters=None, sigmas=None, factors=None, ret_metric=False, moving_mask=None, static_mask=None, **metric_kwargs)
Implements a translation transform. Based on affine_registration().
rigid
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dipy.align._public.rigid(moving, static, moving_affine=None, static_affine=None, *, pipeline=['rigid'], starting_affine=None, metric='MI', level_iters=None, sigmas=None, factors=None, ret_metric=False, moving_mask=None, static_mask=None, **metric_kwargs)
Implements a rigid transform. Based on affine_registration().
rigid_isoscaling
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dipy.align._public.rigid_isoscaling(moving, static, moving_affine=None, static_affine=None, *, pipeline=['rigid_isoscaling'], starting_affine=None, metric='MI', level_iters=None, sigmas=None, factors=None, ret_metric=False, moving_mask=None, static_mask=None, **metric_kwargs)
Implements a rigid isoscaling transform. Based on affine_registration().
rigid_scaling
-
dipy.align._public.rigid_scaling(moving, static, moving_affine=None, static_affine=None, *, pipeline=['rigid_scaling'], starting_affine=None, metric='MI', level_iters=None, sigmas=None, factors=None, ret_metric=False, moving_mask=None, static_mask=None, **metric_kwargs)
Implements a rigid scaling transform. Based on affine_registration().
affine
-
dipy.align._public.affine(moving, static, moving_affine=None, static_affine=None, *, pipeline=['affine'], starting_affine=None, metric='MI', level_iters=None, sigmas=None, factors=None, ret_metric=False, moving_mask=None, static_mask=None, **metric_kwargs)
Implements an affine transform. Based on affine_registration().
_METHOD_DICT
-
dipy.align._public._METHOD_DICT()
dict() -> new empty dictionary
dict(mapping) -> new dictionary initialized from a mapping object’s
- dict(iterable) -> new dictionary initialized as if via:
d = {}
for k, v in iterable:
- dict(**kwargs) -> new dictionary initialized with the name=value pairs
in the keyword argument list. For example: dict(one=1, two=2)
register_series
-
dipy.align._public.register_series(series, ref, pipeline=None, series_affine=None, ref_affine=None, static_mask=None)
Register a series to a reference image.
Parameters
- series4D array or nib.Nifti1Image class instance or str
The data is 4D with the last dimension separating different 3D volumes
- refint or 3D array or nib.Nifti1Image class instance or str
If this is an int, this is the index of the reference image within the
series. Otherwise it is an array of data to register with (associated
with a ref_affine required) or a nifti img or full path to a file
containing one.
- pipelinesequence, optional
Sequence of transforms to do for each volume in the series.
Default: (executed from left to right):
[center_of_mass, translation, rigid, affine]
- series_affine, ref_affine4x4 arrays, optional.
The affine. If provided, this input will over-ride the affine provided
together with the nifti img or file.
- static_maskarray, shape (S, R, C) or (R, C), optional
static image mask that defines which pixels in the static image
are used to calculate the mutual information.
Returns
xformed, affines : 4D array with transformed data and a (4,4,n) array
with 4x4 matrices associated with each of the volumes of the input moving
data that was used to transform it into register with the static data.
register_dwi_series
-
dipy.align._public.register_dwi_series(data, gtab, affine=None, b0_ref=0, pipeline=None, static_mask=None)
Register a DWI series to the mean of the B0 images in that series.
all first registered to the first B0 volume
Parameters
- data4D array or nibabel Nifti1Image class instance or str
Diffusion data. Either as a 4D array or as a nifti image object, or as
a string containing the full path to a nifti file.
- gtaba GradientTable class instance or tuple of strings
If provided as a tuple of strings, these are assumed to be full paths
to the bvals and bvecs files (in that order).
- affine4x4 array, optional.
Must be provided for data provided as an array. If provided together
with Nifti1Image or str data, this input will over-ride the affine
that is stored in the data input. Default: use the affine stored
in data.
- b0_refint, optional.
Which b0 volume to use as reference. Default: 0
- pipelinelist of callables, optional.
The transformations to perform in sequence (from left to right):
Default: [center_of_mass, translation, rigid, affine]
- static_maskarray, shape (S, R, C) or (R, C), optional
static image mask that defines which pixels in the static image
are used to calculate the mutual information.
Returns
xform_img, affine_array: a Nifti1Image containing the registered data and
using the affine of the original data and a list containing the affine
transforms associated with each of the
motion_correction
-
dipy.align._public.motion_correction(data, gtab, affine=None, b0_ref=0, *, pipeline=['center_of_mass', 'translation', 'rigid', 'affine'], static_mask=None)
Apply a motion correction to a DWI dataset (Between-Volumes Motion correction)
Parameters
- data4D array or nibabel Nifti1Image class instance or str
Diffusion data. Either as a 4D array or as a nifti image object, or as
a string containing the full path to a nifti file.
- gtaba GradientTable class instance or tuple of strings
If provided as a tuple of strings, these are assumed to be full paths
to the bvals and bvecs files (in that order).
- affine4x4 array, optional.
Must be provided for data provided as an array. If provided together
with Nifti1Image or str data, this input will over-ride the affine
that is stored in the data input. Default: use the affine stored
in data.
- b0_refint, optional.
Which b0 volume to use as reference. Default: 0
- pipelinelist of callables, optional.
The transformations to perform in sequence (from left to right):
Default: [center_of_mass, translation, rigid, affine]
- static_maskarray, shape (S, R, C) or (R, C), optional
static image mask that defines which pixels in the static image
are used to calculate the mutual information.
Returns
xform_img, affine_array: a Nifti1Image containing the registered data and
using the affine of the original data and a list containing the affine
transforms associated with each of the
streamline_registration
-
dipy.align._public.streamline_registration(moving, static, n_points=100, native_resampled=False)
Register two collections of streamlines (‘bundles’) to each other.
Parameters
- moving, staticlists of 3 by n, or str
The two bundles to be registered. Given either as lists of arrays with
3D coordinates, or strings containing full paths to these files.
- n_pointsint, optional
How many points to resample to. Default: 100.
- native_resampledbool, optional
Whether to return the moving bundle in the original space, but
resampled in the static space to n_points.
Returns
- alignedlist
Streamlines from the moving group, moved to be closely matched to
the static group.
- matrixarray (4, 4)
The affine transformation that takes us from ‘moving’ to ‘static’
gaussian_kernel
-
dipy.align.cpd.gaussian_kernel(X, beta, Y=None)
low_rank_eigen
-
dipy.align.cpd.low_rank_eigen(G, num_eig)
Calculate num_eig eigenvectors and eigenvalues of gaussian matrix G.
Enables lower dimensional solving.
initialize_sigma2
-
dipy.align.cpd.initialize_sigma2(X, Y)
Initialize the variance (sigma2).
Parameters
- X: numpy array
NxD array of points for target.
- Y: numpy array
MxD array of points for source.
Returns
- sigma2: float
Initial variance.
lowrankQS
-
dipy.align.cpd.lowrankQS(G, beta, num_eig, eig_fgt=False)
Calculate eigenvectors and eigenvalues of gaussian matrix G.
!!!
This function is a placeholder for implementing the fast
gauss transform. It is not yet implemented.
!!!
Parameters
- G: numpy array
Gaussian kernel matrix.
- beta: float
Width of the Gaussian kernel.
- num_eig: int
Number of eigenvectors to use in lowrank calculation of G
- eig_fgt: bool
If True, use fast gauss transform method to speed up.
-
class dipy.align.imaffine.AffineInversionError
Bases: Exception
-
__init__(*args, **kwargs)
-
class dipy.align.imaffine.AffineInvalidValuesError
Bases: Exception
-
__init__(*args, **kwargs)
-
class dipy.align.imaffine.AffineMap(affine, domain_grid_shape=None, domain_grid2world=None, codomain_grid_shape=None, codomain_grid2world=None)
Bases: object
-
__init__(affine, domain_grid_shape=None, domain_grid2world=None, codomain_grid_shape=None, codomain_grid2world=None)
AffineMap.
Implements an affine transformation whose domain is given by
domain_grid and domain_grid2world, and whose co-domain is
given by codomain_grid and codomain_grid2world.
The actual transform is represented by the affine matrix, which
operate in world coordinates. Therefore, to transform a moving image
towards a static image, we first map each voxel (i,j,k) of the static
image to world coordinates (x,y,z) by applying domain_grid2world.
Then we apply the affine transform to (x,y,z) obtaining (x’, y’, z’)
in moving image’s world coordinates. Finally, (x’, y’, z’) is mapped
to voxel coordinates (i’, j’, k’) in the moving image by multiplying
(x’, y’, z’) by the inverse of codomain_grid2world. The
codomain_grid_shape is used analogously to transform the static
image towards the moving image when calling transform_inverse.
If the domain/co-domain information is not provided (None) then the
sampling information needs to be specified each time the transform
or transform_inverse is called to transform images. Note that such
sampling information is not necessary to transform points defined in
physical space, such as stream lines.
Parameters
- affinearray, shape (dim + 1, dim + 1)
the matrix defining the affine transform, where dim is the
dimension of the space this map operates in (2 for 2D images,
3 for 3D images). If None, then self represents the identity
transformation.
- domain_grid_shapesequence, shape (dim,), optional
the shape of the default domain sampling grid. When transform
is called to transform an image, the resulting image will have
this shape, unless a different sampling information is provided.
If None, then the sampling grid shape must be specified each time
the transform method is called.
- domain_grid2worldarray, shape (dim + 1, dim + 1), optional
the grid-to-world transform associated with the domain grid.
If None (the default), then the grid-to-world transform is assumed
to be the identity.
- codomain_grid_shapesequence of integers, shape (dim,)
the shape of the default co-domain sampling grid. When
transform_inverse is called to transform an image, the resulting
image will have this shape, unless a different sampling
information is provided. If None (the default), then the sampling
grid shape must be specified each time the transform_inverse
method is called.
- codomain_grid2worldarray, shape (dim + 1, dim + 1)
the grid-to-world transform associated with the co-domain grid.
If None (the default), then the grid-to-world transform is assumed
to be the identity.
-
get_affine()
Return the value of the transformation, not a reference.
Returns
- affinendarray
Copy of the transform, not a reference.
-
set_affine(affine)
Set the affine transform (operating in physical space).
Also sets self.affine_inv - the inverse of affine, or None if
there is no inverse.
Parameters
- affinearray, shape (dim + 1, dim + 1)
the matrix representing the affine transform operating in
physical space. The domain and co-domain information
remains unchanged. If None, then self represents the identity
transformation.
-
transform(image, interpolation='linear', image_grid2world=None, sampling_grid_shape=None, sampling_grid2world=None, resample_only=False)
Transform the input image from co-domain to domain space.
By default, the transformed image is sampled at a grid defined by
self.domain_shape and self.domain_grid2world. If such
information was not provided then sampling_grid_shape is mandatory.
Parameters
- image2D or 3D array
the image to be transformed
- interpolationstring, either ‘linear’ or ‘nearest’
the type of interpolation to be used, either ‘linear’
(for k-linear interpolation) or ‘nearest’ for nearest neighbor
- image_grid2worldarray, shape (dim + 1, dim + 1), optional
the grid-to-world transform associated with image.
If None (the default), then the grid-to-world transform is assumed
to be the identity.
- sampling_grid_shapesequence, shape (dim,), optional
the shape of the grid where the transformed image must be sampled.
If None (the default), then self.codomain_shape is used instead
(which must have been set at initialization, otherwise an exception
will be raised).
- sampling_grid2worldarray, shape (dim + 1, dim + 1), optional
the grid-to-world transform associated with the sampling grid
(specified by sampling_grid_shape, or by default
self.codomain_shape). If None (the default), then the
grid-to-world transform is assumed to be the identity.
- resample_onlyBoolean, optional
If False (the default) the affine transform is applied normally.
If True, then the affine transform is not applied, and the input
image is just re-sampled on the domain grid of this transform.
Returns
- transformedarray, shape sampling_grid_shape or
the transformed image, sampled at the requested grid
-
transform_inverse(image, interpolation='linear', image_grid2world=None, sampling_grid_shape=None, sampling_grid2world=None, resample_only=False)
Transform the input image from domain to co-domain space.
By default, the transformed image is sampled at a grid defined by
self.codomain_shape and self.codomain_grid2world. If such
information was not provided then sampling_grid_shape is mandatory.
Parameters
- image2D or 3D array
the image to be transformed
- interpolationstring, either ‘linear’ or ‘nearest’
the type of interpolation to be used, either ‘linear’
(for k-linear interpolation) or ‘nearest’ for nearest neighbor
- image_grid2worldarray, shape (dim + 1, dim + 1), optional
the grid-to-world transform associated with image.
If None (the default), then the grid-to-world transform is assumed
to be the identity.
- sampling_grid_shapesequence, shape (dim,), optional
the shape of the grid where the transformed image must be sampled.
If None (the default), then self.codomain_shape is used instead
(which must have been set at initialization, otherwise an exception
will be raised).
- sampling_grid2worldarray, shape (dim + 1, dim + 1), optional
the grid-to-world transform associated with the sampling grid
(specified by sampling_grid_shape, or by default
self.codomain_shape). If None (the default), then the
grid-to-world transform is assumed to be the identity.
- resample_onlyBoolean, optional
If False (the default) the affine transform is applied normally.
If True, then the affine transform is not applied, and the input
image is just re-sampled on the domain grid of this transform.
Returns
- transformedarray, shape sampling_grid_shape or
the transformed image, sampled at the requested grid
-
class dipy.align.imaffine.AffineRegistration(metric=None, level_iters=None, sigmas=None, factors=None, method='L-BFGS-B', ss_sigma_factor=None, options=None, verbosity=1)
Bases: object
-
__init__(metric=None, level_iters=None, sigmas=None, factors=None, method='L-BFGS-B', ss_sigma_factor=None, options=None, verbosity=1)
Initialize an instance of the AffineRegistration class.
Parameters
———-
metric : None or object, optional
an instance of a metric. The default is None, implying
the Mutual Information metric with default settings.
level_iters : sequence, optional
the number of iterations at each scale of the scale space.
level_iters[0] corresponds to the coarsest scale,
level_iters[-1] the finest, where n is the length of the
sequence. By default, a 3-level scale space with iterations
sequence equal to [10000, 1000, 100] will be used.
sigmas : sequence of floats, optional
custom smoothing parameter to build the scale space (one parameter
for each scale). By default, the sequence of sigmas will be
[3, 1, 0].
factors : sequence of floats, optional
custom scale factors to build the scale space (one factor for each
scale). By default, the sequence of factors will be [4, 2, 1].
method : string, optional
optimization method to be used. If Scipy version < 0.12, then
only L-BFGS-B is available. Otherwise, method can be any
gradient-based method available in dipy.core.Optimize: CG, BFGS,
Newton-CG, dogleg or trust-ncg.
The default is ‘L-BFGS-B’.
ss_sigma_factor : float, optional
If None, this parameter is not used and an isotropic scale
space with the given factors and sigmas will be built.
If not None, an anisotropic scale space will be used by
automatically selecting the smoothing sigmas along each axis
according to the voxel dimensions of the given image.
The ss_sigma_factor is used to scale the automatically computed
sigmas. For example, in the isotropic case, the sigma of the
kernel will be \(factor * (2 ^ i)\) where
\(i = 1, 2, ..., n_scales - 1\) is the scale (the finest resolution
image \(i=0\) is never smoothed). The default is None.
options : dict, optional
extra optimization options. The default is None, implying
no extra options are passed to the optimizer.
verbosity: int (one of {0, 1, 2, 3}), optional
Set the verbosity level of the algorithm:
0 : do not print anything
1 : print information about the current status of the algorithm
2 : print high level information of the components involved in
the registration that can be used to detect a failing
component.
3 : print as much information as possible to isolate the cause
of a bug.
Default: 1
-
docstring_addendum = 'verbosity: int (one of {0, 1, 2, 3}), optional\n Set the verbosity level of the algorithm:\n 0 : do not print anything\n 1 : print information about the current status of the algorithm\n 2 : print high level information of the components involved in\n the registration that can be used to detect a failing\n component.\n 3 : print as much information as possible to isolate the cause\n of a bug.\n Default: 1\n '
-
optimize(static, moving, transform, params0, static_grid2world=None, moving_grid2world=None, starting_affine=None, ret_metric=False, static_mask=None, moving_mask=None)
Start the optimization process.
Parameters
- static2D or 3D array
the image to be used as reference during optimization.
- moving2D or 3D array
the image to be used as “moving” during optimization. It is
necessary to pre-align the moving image to ensure its domain
lies inside the domain of the deformation fields. This is assumed
to be accomplished by “pre-aligning” the moving image towards the
static using an affine transformation given by the
‘starting_affine’ matrix
- transforminstance of Transform
the transformation with respect to whose parameters the gradient
must be computed
- params0array, shape (n,)
parameters from which to start the optimization. If None, the
optimization will start at the identity transform. n is the
number of parameters of the specified transformation.
- static_grid2worldarray, shape (dim+1, dim+1), optional
the voxel-to-space transformation associated with the static
image. The default is None, implying the transform is the
identity.
- moving_grid2worldarray, shape (dim+1, dim+1), optional
the voxel-to-space transformation associated with the moving
image. The default is None, implying the transform is the
identity.
- starting_affinestring, or matrix, or None, optional
- If string:
‘mass’: align centers of gravity
‘voxel-origin’: align physical coordinates of voxel (0,0,0)
‘centers’: align physical coordinates of central voxels
- If matrix:
array, shape (dim+1, dim+1).
- If None:
Start from identity.
The default is None.
- ret_metricboolean, optional
if True, it returns the parameters for measuring the
similarity between the images (default ‘False’).
The metric containing optimal parameters and
the distance between the images.
- static_maskarray, shape (S, R, C) or (R, C), optional
static image mask that defines which pixels in the static image
are used to calculate the mutual information.
- moving_maskarray, shape (S’, R’, C’) or (R’, C’), optional
moving image mask that defines which pixels in the moving image
are used to calculate the mutual information.
Returns
- affine_mapinstance of AffineMap
the affine resulting affine transformation
- xoptoptimal parameters
the optimal parameters (translation, rotation shear etc.)
- foptSimilarity metric
the value of the function at the optimal parameters.
-
class dipy.align.imwarp.DiffeomorphicMap(dim, disp_shape, disp_grid2world=None, domain_shape=None, domain_grid2world=None, codomain_shape=None, codomain_grid2world=None, prealign=None)
Bases: object
-
__init__(dim, disp_shape, disp_grid2world=None, domain_shape=None, domain_grid2world=None, codomain_shape=None, codomain_grid2world=None, prealign=None)
DiffeomorphicMap
Implements a diffeomorphic transformation on the physical space. The
deformation fields encoding the direct and inverse transformations
share the same domain discretization (both the discretization grid
shape and voxel-to-space matrix). The input coordinates (physical
coordinates) are first aligned using prealign, and then displaced
using the corresponding vector field interpolated at the aligned
coordinates.
Parameters
- dimint, 2 or 3
the transformation’s dimension
- disp_shapearray, shape (dim,)
the number of slices (if 3D), rows and columns of the deformation
field’s discretization
- disp_grid2worldthe voxel-to-space transform between the def. fields
grid and space
- domain_shapearray, shape (dim,)
the number of slices (if 3D), rows and columns of the default
discretization of this map’s domain
- domain_grid2worldarray, shape (dim+1, dim+1)
the default voxel-to-space transformation between this map’s
discretization and physical space
- codomain_shapearray, shape (dim,)
the number of slices (if 3D), rows and columns of the images that
are ‘normally’ warped using this transformation in the forward
direction (this will provide default transformation parameters to
warp images under this transformation). By default, we assume that
the inverse transformation is ‘normally’ used to warp images with
the same discretization and voxel-to-space transformation as the
deformation field grid.
- codomain_grid2worldarray, shape (dim+1, dim+1)
the voxel-to-space transformation of images that are ‘normally’
warped using this transformation (in the forward direction).
- prealignarray, shape (dim+1, dim+1)
the linear transformation to be applied to align input images to
the reference space before warping under the deformation field.
-
allocate()
Creates a zero displacement field
Creates a zero displacement field (the identity transformation).
-
compute_inversion_error()
Inversion error of the displacement fields
Estimates the inversion error of the displacement fields by computing
statistics of the residual vectors obtained after composing the forward
and backward displacement fields.
Returns
- residualarray, shape (R, C) or (S, R, C)
the displacement field resulting from composing the forward and
backward displacement fields of this transformation (the residual
should be zero for a perfect diffeomorphism)
- statsarray, shape (3,)
statistics from the norms of the vectors of the residual
displacement field: maximum, mean and standard deviation
Notes
Since the forward and backward displacement fields have the same
discretization, the final composition is given by
comp[i] = forward[ i + Dinv * backward[i]]
where Dinv is the space-to-grid transformation of the displacement
fields
-
expand_fields(expand_factors, new_shape)
Expands the displacement fields from current shape to new_shape
Up-samples the discretization of the displacement fields to be of
new_shape shape.
Parameters
- expand_factorsarray, shape (dim,)
the factors scaling current spacings (voxel sizes) to spacings in
the expanded discretization.
- new_shapearray, shape (dim,)
the shape of the arrays holding the up-sampled discretization
-
get_backward_field()
Deformation field to transform an image in the backward direction
Returns the deformation field that must be used to warp an image under
this transformation in the backward direction (note the ‘is_inverse’
flag).
-
get_forward_field()
Deformation field to transform an image in the forward direction
Returns the deformation field that must be used to warp an image under
this transformation in the forward direction (note the ‘is_inverse’
flag).
-
get_simplified_transform()
Constructs a simplified version of this Diffeomorhic Map
The simplified version incorporates the pre-align transform, as well as
the domain and codomain affine transforms into the displacement field.
The resulting transformation may be regarded as operating on the
image spaces given by the domain and codomain discretization. As a
result, self.prealign, self.disp_grid2world, self.domain_grid2world and
self.codomain affine will be None (denoting Identity) in the resulting
diffeomorphic map.
-
interpret_matrix(obj)
Try to interpret obj as a matrix
Some operations are performed faster if we know in advance if a matrix
is the identity (so we can skip the actual matrix-vector
multiplication). This function returns None if the given object
is None or the ‘identity’ string. It returns the same object if it is
a numpy array. It raises an exception otherwise.
Parameters
- objobject
any object
Returns
- objobject
the same object given as argument if obj is None or a numpy
array. None if obj is the ‘identity’ string.
-
inverse()
Inverse of this DiffeomorphicMap instance
Returns a diffeomorphic map object representing the inverse of this
transformation. The internal arrays are not copied but just referenced.
Returns
- invDiffeomorphicMap object
the inverse of this diffeomorphic map.
-
shallow_copy()
Shallow copy of this DiffeomorphicMap instance
Creates a shallow copy of this diffeomorphic map (the arrays are not
copied but just referenced)
Returns
- new_mapDiffeomorphicMap object
the shallow copy of this diffeomorphic map
-
transform(image, interpolation='linear', image_world2grid=None, out_shape=None, out_grid2world=None)
Warps an image in the forward direction
Transforms the input image under this transformation in the forward
direction. It uses the “is_inverse” flag to switch between “forward”
and “backward” (if is_inverse is False, then transform(…) warps the
image forwards, else it warps the image backwards).
Parameters
- imagearray, shape (s, r, c) if dim = 3 or (r, c) if dim = 2
the image to be warped under this transformation in the forward
direction
- interpolationstring, either ‘linear’ or ‘nearest’
the type of interpolation to be used for warping, either ‘linear’
(for k-linear interpolation) or ‘nearest’ for nearest neighbor
- image_world2gridarray, shape (dim+1, dim+1)
the transformation bringing world (space) coordinates to voxel
coordinates of the image given as input
- out_shapearray, shape (dim,)
the number of slices, rows and columns of the desired warped image
- out_grid2worldthe transformation bringing voxel coordinates of the
warped image to physical space
Returns
- warpedarray, shape = out_shape or self.codomain_shape if None
the warped image under this transformation in the forward direction
Notes
See _warp_forward and _warp_backward documentation for further
information.
-
transform_inverse(image, interpolation='linear', image_world2grid=None, out_shape=None, out_grid2world=None)
Warps an image in the backward direction
Transforms the input image under this transformation in the backward
direction. It uses the “is_inverse” flag to switch between “forward”
and “backward” (if is_inverse is False, then transform_inverse(…)
warps the image backwards, else it warps the image forwards)
Parameters
- imagearray, shape (s, r, c) if dim = 3 or (r, c) if dim = 2
the image to be warped under this transformation in the forward
direction
- interpolationstring, either ‘linear’ or ‘nearest’
the type of interpolation to be used for warping, either ‘linear’
(for k-linear interpolation) or ‘nearest’ for nearest neighbor
- image_world2gridarray, shape (dim+1, dim+1)
the transformation bringing world (space) coordinates to voxel
coordinates of the image given as input
- out_shapearray, shape (dim,)
the number of slices, rows, and columns of the desired warped image
- out_grid2worldthe transformation bringing voxel coordinates of the
warped image to physical space
Returns
- warpedarray, shape = out_shape or self.codomain_shape if None
warped image under this transformation in the backward direction
Notes
See _warp_forward and _warp_backward documentation for further
information.
-
transform_points(points, coord2world=None, world2coord=None)
-
transform_points_inverse(points, coord2world=None, world2coord=None)
-
warp_endomorphism(phi)
Composition of this DiffeomorphicMap with a given endomorphism
Creates a new DiffeomorphicMap C with the same properties as self and
composes its displacement fields with phi’s corresponding fields.
The resulting diffeomorphism is of the form C(x) = phi(self(x)) with
inverse C^{-1}(y) = self^{-1}(phi^{-1}(y)). We assume that phi is an
endomorphism with the same discretization and domain affine as self
to ensure that the composition inherits self’s properties (we also
assume that the pre-aligning matrix of phi is None or identity).
Parameters
- phiDiffeomorphicMap object
the endomorphism to be warped by this diffeomorphic map
Returns
- compositionthe composition of this diffeomorphic map with the
endomorphism given as input
Notes
The problem with our current representation of a DiffeomorphicMap is
that the set of Diffeomorphism that can be represented this way (a
pre-aligning matrix followed by a non-linear endomorphism given as a
displacement field) is not closed under the composition operation.
Supporting a general DiffeomorphicMap class, closed under composition,
may be extremely costly computationally, and the kind of
transformations we actually need for Avants’ mid-point algorithm (SyN)
are much simpler.
-
class dipy.align.imwarp.DiffeomorphicRegistration(metric=None)
Bases: object
-
__init__(metric=None)
Diffeomorphic Registration
This abstract class defines the interface to be implemented by any
optimization algorithm for diffeomorphic registration.
Parameters
- metricSimilarityMetric object
the object measuring the similarity of the two images. The
registration algorithm will minimize (or maximize) the provided
similarity.
-
abstract get_map()
Returns the resulting diffeomorphic map after optimization
-
abstract optimize()
Starts the metric optimization
This is the main function each specialized class derived from this must
implement. Upon completion, the deformation field must be available
from the forward transformation model.
-
set_level_iters(level_iters)
Sets the number of iterations at each pyramid level
Establishes the maximum number of iterations to be performed at each
level of the Gaussian pyramid, similar to ANTS.
Parameters
- level_iterslist
the number of iterations at each level of the Gaussian pyramid.
level_iters[0] corresponds to the finest level, level_iters[n-1]
the coarsest, where n is the length of the list
-
class dipy.align.imwarp.SymmetricDiffeomorphicRegistration(metric, level_iters=None, step_length=0.25, ss_sigma_factor=0.2, opt_tol=1e-05, inv_iter=20, inv_tol=0.001, callback=None)
Bases: DiffeomorphicRegistration
-
__init__(metric, level_iters=None, step_length=0.25, ss_sigma_factor=0.2, opt_tol=1e-05, inv_iter=20, inv_tol=0.001, callback=None)
Symmetric Diffeomorphic Registration (SyN) Algorithm
Performs the multi-resolution optimization algorithm for non-linear
registration using a given similarity metric.
Parameters
- metricSimilarityMetric object
the metric to be optimized
- level_iterslist of int
the number of iterations at each level of the Gaussian Pyramid (the
length of the list defines the number of pyramid levels to be
used)
- opt_tolfloat
the optimization will stop when the estimated derivative of the
energy profile w.r.t. time falls below this threshold
- inv_iterint
the number of iterations to be performed by the displacement field
inversion algorithm
- step_lengthfloat
the length of the maximum displacement vector of the update
displacement field at each iteration
- ss_sigma_factorfloat
parameter of the scale-space smoothing kernel. For example, the
std. dev. of the kernel will be factor*(2^i) in the isotropic case
where i = 0, 1, …, n_scales is the scale
- inv_tolfloat
the displacement field inversion algorithm will stop iterating
when the inversion error falls below this threshold
- callbackfunction(SymmetricDiffeomorphicRegistration)
a function receiving a SymmetricDiffeomorphicRegistration object
to be called after each iteration (this optimizer will call this
function passing self as parameter)
-
get_map()
Return the resulting diffeomorphic map.
Returns the DiffeomorphicMap registering the moving image towards
the static image.
-
optimize(static, moving, static_grid2world=None, moving_grid2world=None, prealign=None)
Starts the optimization
Parameters
- staticarray, shape (S, R, C) or (R, C)
the image to be used as reference during optimization. The
displacement fields will have the same discretization as the static
image.
- movingarray, shape (S, R, C) or (R, C)
the image to be used as “moving” during optimization. Since the
deformation fields’ discretization is the same as the static image,
it is necessary to pre-align the moving image to ensure its domain
lies inside the domain of the deformation fields. This is assumed
to be accomplished by “pre-aligning” the moving image towards the
static using an affine transformation given by the ‘prealign’
matrix
- static_grid2worldarray, shape (dim+1, dim+1)
the voxel-to-space transformation associated to the static image
- moving_grid2worldarray, shape (dim+1, dim+1)
the voxel-to-space transformation associated to the moving image
- prealignarray, shape (dim+1, dim+1)
the affine transformation (operating on the physical space)
pre-aligning the moving image towards the static
Returns
- static_to_refDiffeomorphicMap object
the diffeomorphic map that brings the moving image towards the
static one in the forward direction (i.e. by calling
static_to_ref.transform) and the static image towards the
moving one in the backward direction (i.e. by calling
static_to_ref.transform_inverse).
-
update(current_displacement, new_displacement, disp_world2grid, time_scaling)
Composition of the current displacement field with the given field
Interpolates new displacement at the locations defined by
current_displacement. Equivalently, computes the composition C of the
given displacement fields as C(x) = B(A(x)), where A is
current_displacement and B is new_displacement. This function is
intended to be used with deformation fields of the same sampling
(e.g. to be called by a registration algorithm).
Parameters
- current_displacementarray, shape (R’, C’, 2) or (S’, R’, C’, 3)
the displacement field defining where to interpolate
new_displacement
- new_displacementarray, shape (R, C, 2) or (S, R, C, 3)
the displacement field to be warped by current_displacement
- disp_world2gridarray, shape (dim+1, dim+1)
the space-to-grid transform associated with the displacements’
grid (we assume that both displacements are discretized over the
same grid)
- time_scalingfloat
scaling factor applied to d2. The effect may be interpreted as
moving d1 displacements along a factor (time_scaling) of d2.
Returns
- updatedarray, shape (the same as new_displacement)
the warped displacement field
mean_norm : the mean norm of all vectors in current_displacement
RegistrationStages
-
dipy.align.imwarp.RegistrationStages()
logger
-
dipy.align.imwarp.logger()
Instances of the Logger class represent a single logging channel. A
“logging channel” indicates an area of an application. Exactly how an
“area” is defined is up to the application developer. Since an
application can have any number of areas, logging channels are identified
by a unique string. Application areas can be nested (e.g. an area
of “input processing” might include sub-areas “read CSV files”, “read
XLS files” and “read Gnumeric files”). To cater for this natural nesting,
channel names are organized into a namespace hierarchy where levels are
separated by periods, much like the Java or Python package namespace. So
in the instance given above, channel names might be “input” for the upper
level, and “input.csv”, “input.xls” and “input.gnu” for the sub-levels.
There is no arbitrary limit to the depth of nesting.
mult_aff
-
dipy.align.imwarp.mult_aff(A, B)
Returns the matrix product A.dot(B) considering None as the identity
Parameters
A : array, shape (n,k)
B : array, shape (k,m)
Returns
The matrix product A.dot(B). If any of the input matrices is None, it is
treated as the identity matrix. If both matrices are None, None is returned
get_direction_and_spacings
-
dipy.align.imwarp.get_direction_and_spacings(affine, dim)
Extracts the rotational and spacing components from a matrix
Extracts the rotational and spacing (voxel dimensions) components from a
matrix. An image gradient represents the local variation of the image’s
gray values per voxel. Since we are iterating on the physical space, we
need to compute the gradients as variation per millimeter, so we need to
divide each gradient’s component by the voxel size along the corresponding
axis, that’s what the spacings are used for. Since the image’s gradients
are oriented along the grid axes, we also need to re-orient the gradients
to be given in physical space coordinates.
Parameters
- affinearray, shape (k, k), k = 3, 4
the matrix transforming grid coordinates to physical space.
Returns
- directionarray, shape (k-1, k-1)
the rotational component of the input matrix
- spacingsarray, shape (k-1,)
the scaling component (voxel size) of the matrix
-
class dipy.align.metrics.SimilarityMetric(dim)
Bases: object
-
__init__(dim)
Similarity Metric abstract class
A similarity metric is in charge of keeping track of the numerical
value of the similarity (or distance) between the two given images. It
also computes the update field for the forward and inverse displacement
fields to be used in a gradient-based optimization algorithm. Note that
this metric does not depend on any transformation (affine or
non-linear) so it assumes the static and moving images are already
warped
Parameters
- dimint (either 2 or 3)
the dimension of the image domain
-
abstract compute_backward()
Computes one step bringing the static image towards the moving.
Computes the backward update field to register the static image towards
the moving image in a gradient-based optimization algorithm
-
abstract compute_forward()
Computes one step bringing the reference image towards the static.
Computes the forward update field to register the moving image towards
the static image in a gradient-based optimization algorithm
-
abstract free_iteration()
Releases the resources no longer needed by the metric
This method is called by the RegistrationOptimizer after the required
iterations have been computed (forward and / or backward) so that the
SimilarityMetric can safely delete any data it computed as part of the
initialization
-
abstract get_energy()
Numerical value assigned by this metric to the current image pair
Must return the numeric value of the similarity between the given
static and moving images
-
abstract initialize_iteration()
Prepares the metric to compute one displacement field iteration.
This method will be called before any compute_forward or
compute_backward call, this allows the Metric to pre-compute any useful
information for speeding up the update computations. This
initialization was needed in ANTS because the updates are called once
per voxel. In Python this is unpractical, though.
-
set_levels_above(levels)
Informs the metric how many pyramid levels are above the current one
Informs this metric the number of pyramid levels above the current one.
The metric may change its behavior (e.g. number of inner iterations)
accordingly
Parameters
- levelsint
the number of levels above the current Gaussian Pyramid level
-
set_levels_below(levels)
Informs the metric how many pyramid levels are below the current one
Informs this metric the number of pyramid levels below the current one.
The metric may change its behavior (e.g. number of inner iterations)
accordingly
Parameters
- levelsint
the number of levels below the current Gaussian Pyramid level
-
set_moving_image(moving_image, moving_affine, moving_spacing, moving_direction)
Sets the moving image being compared against the static one.
Sets the moving image. The default behavior (of this abstract class) is
simply to assign the reference to an attribute, but
generalizations of the metric may need to perform other operations
Parameters
- moving_imagearray, shape (R, C) or (S, R, C)
the moving image
-
set_static_image(static_image, static_affine, static_spacing, static_direction)
Sets the static image being compared against the moving one.
Sets the static image. The default behavior (of this abstract class) is
simply to assign the reference to an attribute, but
generalizations of the metric may need to perform other operations
Parameters
- static_imagearray, shape (R, C) or (S, R, C)
the static image
-
use_moving_image_dynamics(original_moving_image, transformation)
This is called by the optimizer just after setting the moving image
This method allows the metric to compute any useful
information from knowing how the current static image was generated
(as the transformation of an original static image). This method is
called by the optimizer just after it sets the static image.
Transformation will be an instance of DiffeomorficMap or None if
the original_moving_image equals self.moving_image.
Parameters
- original_moving_imagearray, shape (R, C) or (S, R, C)
original image from which the current moving image was generated
- transformationDiffeomorphicMap object
the transformation that was applied to the original image to generate
the current moving image
-
use_static_image_dynamics(original_static_image, transformation)
This is called by the optimizer just after setting the static image.
This method allows the metric to compute any useful
information from knowing how the current static image was generated
(as the transformation of an original static image). This method is
called by the optimizer just after it sets the static image.
Transformation will be an instance of DiffeomorficMap or None
if the original_static_image equals self.moving_image.
Parameters
- original_static_imagearray, shape (R, C) or (S, R, C)
original image from which the current static image was generated
- transformationDiffeomorphicMap object
the transformation that was applied to original image to generate
the current static image
-
class dipy.align.metrics.CCMetric(dim, sigma_diff=2.0, radius=4)
Bases: SimilarityMetric
-
__init__(dim, sigma_diff=2.0, radius=4)
Normalized Cross-Correlation Similarity metric.
Parameters
- dimint (either 2 or 3)
the dimension of the image domain
- sigma_diffthe standard deviation of the Gaussian smoothing kernel to
be applied to the update field at each iteration
- radiusint
the radius of the squared (cubic) neighborhood at each voxel to be
considered to compute the cross correlation
-
compute_backward()
Computes one step bringing the static image towards the moving.
Computes the update displacement field to be used for registration of
the static image towards the moving image
-
compute_forward()
Computes one step bringing the moving image towards the static.
Computes the update displacement field to be used for registration of
the moving image towards the static image
-
free_iteration()
Frees the resources allocated during initialization
-
get_energy()
Numerical value assigned by this metric to the current image pair
Returns the Cross Correlation (data term) energy computed at the
largest iteration
-
initialize_iteration()
Prepares the metric to compute one displacement field iteration.
Pre-computes the cross-correlation factors for efficient computation
of the gradient of the Cross Correlation w.r.t. the displacement field.
It also pre-computes the image gradients in the physical space by
re-orienting the gradients in the voxel space using the corresponding
affine transformations.
-
class dipy.align.metrics.EMMetric(dim, smooth=1.0, inner_iter=5, q_levels=256, double_gradient=True, step_type='gauss_newton')
Bases: SimilarityMetric
-
__init__(dim, smooth=1.0, inner_iter=5, q_levels=256, double_gradient=True, step_type='gauss_newton')
Expectation-Maximization Metric
Similarity metric based on the Expectation-Maximization algorithm to
handle multi-modal images. The transfer function is modeled as a set of
hidden random variables that are estimated at each iteration of the
algorithm.
Parameters
- dimint (either 2 or 3)
the dimension of the image domain
- smoothfloat
smoothness parameter, the larger the value the smoother the
deformation field
- inner_iterint
number of iterations to be performed at each level of the multi-
resolution Gauss-Seidel optimization algorithm (this is not the
number of steps per Gaussian Pyramid level, that parameter must
be set for the optimizer, not the metric)
- q_levelsnumber of quantization levels (equal to the number of hidden
variables in the EM algorithm)
- double_gradientboolean
if True, the gradient of the expected static image under the moving
modality will be added to the gradient of the moving image,
similarly, the gradient of the expected moving image under the
static modality will be added to the gradient of the static image.
- step_typestring (‘gauss_newton’, ‘demons’)
the optimization schedule to be used in the multi-resolution
Gauss-Seidel optimization algorithm (not used if Demons Step is
selected)
-
compute_backward()
Computes one step bringing the static image towards the moving.
Computes the update displacement field to be used for registration of
the static image towards the moving image
-
compute_demons_step(forward_step=True)
Demons step for EM metric
Parameters
- forward_stepboolean
if True, computes the Demons step in the forward direction
(warping the moving towards the static image). If False,
computes the backward step (warping the static image to the
moving image)
Returns
- displacementarray, shape (R, C, 2) or (S, R, C, 3)
the Demons step
-
compute_forward()
Computes one step bringing the reference image towards the static.
Computes the forward update field to register the moving image towards
the static image in a gradient-based optimization algorithm
-
compute_gauss_newton_step(forward_step=True)
Computes the Gauss-Newton energy minimization step
Computes the Newton step to minimize this energy, i.e., minimizes the
linearized energy function with respect to the
regularized displacement field (this step does not require
post-smoothing, as opposed to the demons step, which does not include
regularization). To accelerate convergence we use the multi-grid
Gauss-Seidel algorithm proposed by Bruhn and Weickert et al [Bruhn05]
Parameters
- forward_stepboolean
if True, computes the Newton step in the forward direction
(warping the moving towards the static image). If False,
computes the backward step (warping the static image to the
moving image)
Returns
- displacementarray, shape (R, C, 2) or (S, R, C, 3)
the Newton step
References
- [Bruhn05] Andres Bruhn and Joachim Weickert, “Towards ultimate motion
estimation: combining highest accuracy with real-time
performance”, 10th IEEE International Conference on Computer
Vision, 2005. ICCV 2005.
-
free_iteration()
Frees the resources allocated during initialization
-
get_energy()
The numerical value assigned by this metric to the current image pair
Returns the EM (data term) energy computed at the largest
iteration
-
initialize_iteration()
Prepares the metric to compute one displacement field iteration.
Pre-computes the transfer functions (hidden random variables) and
variances of the estimators. Also pre-computes the gradient of both
input images. Note that once the images are transformed to the opposite
modality, the gradient of the transformed images can be used with the
gradient of the corresponding modality in the same fashion as
diff-demons does for mono-modality images. If the flag
self.use_double_gradient is True these gradients are averaged.
-
use_moving_image_dynamics(original_moving_image, transformation)
This is called by the optimizer just after setting the moving image.
EMMetric takes advantage of the image dynamics by computing the
current moving image mask from the original_moving_image mask (warped
by nearest neighbor interpolation)
Parameters
- original_moving_imagearray, shape (R, C) or (S, R, C)
the original moving image from which the current moving image was
generated, the current moving image is the one that was provided
via ‘set_moving_image(…)’, which may not be the same as the
original moving image but a warped version of it.
- transformationDiffeomorphicMap object
the transformation that was applied to the original_moving_image
to generate the current moving image
-
use_static_image_dynamics(original_static_image, transformation)
This is called by the optimizer just after setting the static image.
EMMetric takes advantage of the image dynamics by computing the
current static image mask from the originalstaticImage mask (warped
by nearest neighbor interpolation)
Parameters
- original_static_imagearray, shape (R, C) or (S, R, C)
the original static image from which the current static image was
generated, the current static image is the one that was provided
via ‘set_static_image(…)’, which may not be the same as the
original static image but a warped version of it (even the static
image changes during Symmetric Normalization, not only the moving
one).
- transformationDiffeomorphicMap object
the transformation that was applied to the original_static_image
to generate the current static image
-
class dipy.align.metrics.SSDMetric(dim, smooth=4, inner_iter=10, step_type='demons')
Bases: SimilarityMetric
-
__init__(dim, smooth=4, inner_iter=10, step_type='demons')
Sum of Squared Differences (SSD) Metric
Similarity metric for (mono-modal) nonlinear image registration defined
by the sum of squared differences (SSD)
Parameters
- dimint (either 2 or 3)
the dimension of the image domain
- smoothfloat
smoothness parameter, the larger the value the smoother the
deformation field
- inner_iterint
number of iterations to be performed at each level of the multi-
resolution Gauss-Seidel optimization algorithm (this is not the
number of steps per Gaussian Pyramid level, that parameter must
be set for the optimizer, not the metric)
- step_typestring
the displacement field step to be computed when ‘compute_forward’
and ‘compute_backward’ are called. Either ‘demons’ or
‘gauss_newton’
-
compute_backward()
Computes one step bringing the static image towards the moving.
Computes the updated displacement field to be used for registration of
the static image towards the moving image
-
compute_demons_step(forward_step=True)
Demons step for SSD metric
Computes the demons step proposed by Vercauteren et al.[Vercauteren09]
for the SSD metric.
Parameters
- forward_stepboolean
if True, computes the Demons step in the forward direction
(warping the moving towards the static image). If False,
computes the backward step (warping the static image to the
moving image)
Returns
- displacementarray, shape (R, C, 2) or (S, R, C, 3)
the Demons step
References
- [Vercauteren09] Tom Vercauteren, Xavier Pennec, Aymeric Perchant,
Nicholas Ayache, “Diffeomorphic Demons: Efficient
Non-parametric Image Registration”, Neuroimage 2009
-
compute_forward()
Computes one step bringing the reference image towards the static.
Computes the update displacement field to be used for registration of
the moving image towards the static image
-
compute_gauss_newton_step(forward_step=True)
Computes the Gauss-Newton energy minimization step
Minimizes the linearized energy function (Newton step) defined by the
sum of squared differences of corresponding pixels of the input images
with respect to the displacement field.
Parameters
- forward_stepboolean
if True, computes the Newton step in the forward direction
(warping the moving towards the static image). If False,
computes the backward step (warping the static image to the
moving image)
Returns
- displacementarray, shape = static_image.shape + (3,)
if forward_step==True, the forward SSD Gauss-Newton step,
else, the backward step
-
free_iteration()
Nothing to free for the SSD metric
-
get_energy()
The numerical value assigned by this metric to the current image pair
Returns the Sum of Squared Differences (data term) energy computed at
the largest iteration
-
initialize_iteration()
Prepares the metric to compute one displacement field iteration.
Pre-computes the gradient of the input images to be used in the
computation of the forward and backward steps.
v_cycle_2d
-
dipy.align.metrics.v_cycle_2d(n, k, delta_field, sigma_sq_field, gradient_field, target, lambda_param, displacement, depth=0)
Multi-resolution Gauss-Seidel solver using V-type cycles
Multi-resolution Gauss-Seidel solver: solves the Gauss-Newton linear system
by first filtering (GS-iterate) the current level, then solves for the
residual at a coarser resolution and finally refines the solution at the
current resolution. This scheme corresponds to the V-cycle proposed by
Bruhn and Weickert[Bruhn05].
Parameters
- nint
number of levels of the multi-resolution algorithm (it will be called
recursively until level n == 0)
- kint
the number of iterations at each multi-resolution level
- delta_fieldarray, shape (R, C)
the difference between the static and moving image (the ‘derivative
w.r.t. time’ in the optical flow model)
- sigma_sq_fieldarray, shape (R, C)
the variance of the gray level value at each voxel, according to the
EM model (for SSD, it is 1 for all voxels). Inf and 0 values
are processed specially to support infinite and zero variance.
- gradient_fieldarray, shape (R, C, 2)
the gradient of the moving image
- targetarray, shape (R, C, 2)
right-hand side of the linear system to be solved in the Weickert’s
multi-resolution algorithm
- lambda_paramfloat
smoothness parameter, the larger its value the smoother the
displacement field
- displacementarray, shape (R, C, 2)
the displacement field to start the optimization from
Returns
- energythe energy of the EM (or SSD if sigmafield[…]==1) metric at this
iteration
References
- [Bruhn05] Andres Bruhn and Joachim Weickert, “Towards ultimate motion
estimation: combining the highest accuracy with real-time
performance”, 10th IEEE International Conference on Computer
Vision, 2005. ICCV 2005.
v_cycle_3d
-
dipy.align.metrics.v_cycle_3d(n, k, delta_field, sigma_sq_field, gradient_field, target, lambda_param, displacement, depth=0)
Multi-resolution Gauss-Seidel solver using V-type cycles
Multi-resolution Gauss-Seidel solver: solves the linear system by first
filtering (GS-iterate) the current level, then solves for the residual
at a coarser resolution and finally refines the solution at the current
resolution. This scheme corresponds to the V-cycle proposed by Bruhn and
Weickert[1].
[1] Andres Bruhn and Joachim Weickert, “Towards ultimate motion estimation:
combining highest accuracy with real-time performance”,
10th IEEE International Conference on Computer Vision, 2005.
ICCV 2005.
Parameters
- nint
number of levels of the multi-resolution algorithm (it will be called
recursively until level n == 0)
- kint
the number of iterations at each multi-resolution level
- delta_fieldarray, shape (S, R, C)
the difference between the static and moving image (the ‘derivative
w.r.t. time’ in the optical flow model)
- sigma_sq_fieldarray, shape (S, R, C)
the variance of the gray level value at each voxel, according to the
EM model (for SSD, it is 1 for all voxels). Inf and 0 values
are processed specially to support infinite and zero variance.
- gradient_fieldarray, shape (S, R, C, 3)
the gradient of the moving image
- targetarray, shape (S, R, C, 3)
right-hand side of the linear system to be solved in the Weickert’s
multi-resolution algorithm
- lambda_paramfloat
smoothness parameter, the larger its value the smoother the
displacement field
- displacementarray, shape (S, R, C, 3)
the displacement field to start the optimization from
Returns
- energythe energy of the EM (or SSD if sigmafield[…]==1) metric at this
iteration
reslice
-
dipy.align.reslice.reslice(data, affine, zooms, new_zooms, order=1, mode='constant', cval=0, num_processes=1)
Reslice data with new voxel resolution defined by new_zooms.
Parameters
- dataarray, shape (I,J,K) or (I,J,K,N)
3d volume or 4d volume with datasets
- affinearray, shape (4,4)
mapping from voxel coordinates to world coordinates
- zoomstuple, shape (3,)
voxel size for (i,j,k) dimensions
- new_zoomstuple, shape (3,)
new voxel size for (i,j,k) after resampling
- orderint, from 0 to 5
order of interpolation for resampling/reslicing,
0 nearest interpolation, 1 trilinear etc..
if you don’t want any smoothing 0 is the option you need.
- modestring (‘constant’, ‘nearest’, ‘reflect’ or ‘wrap’)
Points outside the boundaries of the input are filled according
to the given mode.
- cvalfloat
Value used for points outside the boundaries of the input if
mode=’constant’.
- num_processesint, optional
Split the calculation to a pool of children processes. This only
applies to 4D data arrays. Default is 1. If < 0 the maximal number
of cores minus num_processes + 1 is used (enter -1 to use as many
cores as possible). 0 raises an error.
Returns
- data2array, shape (I,J,K) or (I,J,K,N)
datasets resampled into isotropic voxel size
- affine2array, shape (4,4)
new affine for the resampled image
Examples
>>> from dipy.io.image import load_nifti
>>> from dipy.align.reslice import reslice
>>> from dipy.data import get_fnames
>>> f_name = get_fnames('aniso_vox')
>>> data, affine, zooms = load_nifti(f_name, return_voxsize=True)
>>> data.shape == (58, 58, 24)
True
>>> zooms
(4.0, 4.0, 5.0)
>>> new_zooms = (3.,3.,3.)
>>> new_zooms
(3.0, 3.0, 3.0)
>>> data2, affine2 = reslice(data, affine, zooms, new_zooms)
>>> data2.shape == (77, 77, 40)
True
-
class dipy.align.scalespace.ScaleSpace(image, num_levels, image_grid2world=None, input_spacing=None, sigma_factor=0.2, mask0=False)
Bases: object
-
__init__(image, num_levels, image_grid2world=None, input_spacing=None, sigma_factor=0.2, mask0=False)
ScaleSpace.
Computes the Scale Space representation of an image. The scale space is
simply a list of images produced by smoothing the input image with a
Gaussian kernel with increasing smoothing parameter. If the image’s
voxels are isotropic, the smoothing will be the same along all
directions: at level L = 0, 1, …, the sigma is given by
\(s * ( 2^L - 1 )\).
If the voxel dimensions are not isotropic, then the smoothing is
weaker along low resolution directions.
Parameters
———-
image : array, shape (r,c) or (s, r, c) where s is the number of
slices, r is the number of rows and c is the number of columns of
the input image.
num_levels : int
the desired number of levels (resolutions) of the scale space
image_grid2world : array, shape (dim + 1, dim + 1), optional
the grid-to-space transform of the image grid. The default is
the identity matrix
input_spacing : array, shape (dim,), optional
the spacing (voxel size) between voxels in physical space. The
default is 1.0 along all axes
sigma_factor : float, optional
the smoothing factor to be used in the construction of the scale
space. The default is 0.2
mask0 : Boolean, optional
if True, all smoothed images will be zero at all voxels that are
zero in the input image. The default is False.
-
get_affine(level)
Voxel-to-space transformation at a given level.
Returns the voxel-to-space transformation associated with the
sub-sampled image at a particular resolution of the scale space (note
that this object does not explicitly subsample the smoothed images, but
only provides the properties the sub-sampled images must have).
Parameters
- levelint, 0 <= from_level < L, (L = number of resolutions)
the scale space level to get affine transform from
Returns
the affine (voxel-to-space) transform at the requested resolution
or None if an invalid level was requested
-
get_affine_inv(level)
Space-to-voxel transformation at a given level.
Returns the space-to-voxel transformation associated with the
sub-sampled image at a particular resolution of the scale space (note
that this object does not explicitly subsample the smoothed images, but
only provides the properties the sub-sampled images must have).
Parameters
- levelint, 0 <= from_level < L, (L = number of resolutions)
the scale space level to get the inverse transform from
Returns
the inverse (space-to-voxel) transform at the requested resolution or
None if an invalid level was requested
-
get_domain_shape(level)
Shape the sub-sampled image must have at a particular level.
Returns the shape the sub-sampled image must have at a particular
resolution of the scale space (note that this object does not
explicitly subsample the smoothed images, but only provides the
properties the sub-sampled images must have).
Parameters
- levelint, 0 <= from_level < L, (L = number of resolutions)
the scale space level to get the sub-sampled shape from
Returns
the sub-sampled shape at the requested resolution or None if an
invalid level was requested
-
get_expand_factors(from_level, to_level)
Ratio of voxel size from pyramid level from_level to to_level.
Given two scale space resolutions a = from_level, b = to_level,
returns the ratio of voxels size at level b to voxel size at level a
(the factor that must be used to multiply voxels at level a to
‘expand’ them to level b).
Parameters
- from_levelint, 0 <= from_level < L, (L = number of resolutions)
the resolution to expand voxels from
- to_levelint, 0 <= to_level < from_level
the resolution to expand voxels to
Returns
- factorsarray, shape (k,), k = 2, 3
the expand factors (a scalar for each voxel dimension)
-
get_image(level)
Smoothed image at a given level.
Returns the smoothed image at the requested level in the Scale Space.
Parameters
- levelint, 0 <= from_level < L, (L = number of resolutions)
the scale space level to get the smooth image from
Returns
the smooth image at the requested resolution or None if an invalid
level was requested
-
get_scaling(level)
Adjustment factor for input-spacing to reflect voxel sizes at level.
Returns the scaling factor that needs to be applied to the input
spacing (the voxel sizes of the image at level 0 of the scale space) to
transform them to voxel sizes at the requested level.
Parameters
- levelint, 0 <= from_level < L, (L = number of resolutions)
the scale space level to get the scalings from
Returns
the scaling factors from the original spacing to the spacings at the
requested level
-
get_sigmas(level)
Smoothing parameters used at a given level.
Returns the smoothing parameters (a scalar for each axis) used at the
requested level of the scale space
Parameters
- levelint, 0 <= from_level < L, (L = number of resolutions)
the scale space level to get the smoothing parameters from
Returns
the smoothing parameters at the requested level
-
get_spacing(level)
Spacings the sub-sampled image must have at a particular level.
Returns the spacings (voxel sizes) the sub-sampled image must have at a
particular resolution of the scale space (note that this object does
not explicitly subsample the smoothed images, but only provides the
properties the sub-sampled images must have).
Parameters
- levelint, 0 <= from_level < L, (L = number of resolutions)
the scale space level to get the sub-sampled shape from
Returns
the spacings (voxel sizes) at the requested resolution or None if an
invalid level was requested
-
print_level(level)
Prints properties of a pyramid level.
Prints the properties of a level of this scale space to standard output
Parameters
- levelint, 0 <= from_level < L, (L = number of resolutions)
the scale space level to be printed
-
class dipy.align.scalespace.IsotropicScaleSpace(image, factors, sigmas, image_grid2world=None, input_spacing=None, mask0=False)
Bases: ScaleSpace
-
__init__(image, factors, sigmas, image_grid2world=None, input_spacing=None, mask0=False)
IsotropicScaleSpace.
Computes the Scale Space representation of an image using isotropic
smoothing kernels for all scales. The scale space is simply a list
of images produced by smoothing the input image with a Gaussian
kernel with different smoothing parameters.
This specialization of ScaleSpace allows the user to provide custom
scale and smoothing factors for all scales.
Parameters
- imagearray, shape (r,c) or (s, r, c) where s is the number of
slices, r is the number of rows and c is the number of columns of
the input image.
- factorslist of floats
custom scale factors to build the scale space (one factor for each
scale).
- sigmaslist of floats
custom smoothing parameter to build the scale space (one parameter
for each scale).
- image_grid2worldarray, shape (dim + 1, dim + 1), optional
the grid-to-space transform of the image grid. The default is
the identity matrix.
- input_spacingarray, shape (dim,), optional
the spacing (voxel size) between voxels in physical space. The
default if 1.0 along all axes.
- mask0Boolean, optional
if True, all smoothed images will be zero at all voxels that are
zero in the input image. The default is False.
logger
-
dipy.align.scalespace.logger()
Instances of the Logger class represent a single logging channel. A
“logging channel” indicates an area of an application. Exactly how an
“area” is defined is up to the application developer. Since an
application can have any number of areas, logging channels are identified
by a unique string. Application areas can be nested (e.g. an area
of “input processing” might include sub-areas “read CSV files”, “read
XLS files” and “read Gnumeric files”). To cater for this natural nesting,
channel names are organized into a namespace hierarchy where levels are
separated by periods, much like the Java or Python package namespace. So
in the instance given above, channel names might be “input” for the upper
level, and “input.csv”, “input.xls” and “input.gnu” for the sub-levels.
There is no arbitrary limit to the depth of nesting.
-
class dipy.align.streamlinear.StreamlineDistanceMetric(num_threads=None)
Bases: object
-
__init__(num_threads=None)
An abstract class for the metric used for streamline registration.
If the two sets of streamlines match exactly then method distance
of this object should be minimum.
Parameters
- num_threadsint, optional
Number of threads to be used for OpenMP parallelization. If None
(default) the value of OMP_NUM_THREADS environment variable is used
if it is set, otherwise all available threads are used. If < 0 the
maximal number of threads minus |num_threads + 1| is used (enter -1
to use as many threads as possible). 0 raises an error. Only
metrics using OpenMP will use this variable.
-
abstract distance(xopt)
calculate distance for current set of parameters.
-
abstract setup(static, moving)
-
class dipy.align.streamlinear.BundleMinDistanceMetric(num_threads=None)
Bases: StreamlineDistanceMetric
Bundle-based Minimum Distance aka BMD
This is the cost function used by the StreamlineLinearRegistration.
Methods
setup(static, moving)
distance(xopt)
References
[Garyfallidis14]
Garyfallidis et al., “Direct native-space fiber
bundle alignment for group comparisons”, ISMRM,
2014.
-
__init__(num_threads=None)
An abstract class for the metric used for streamline registration.
If the two sets of streamlines match exactly then method distance
of this object should be minimum.
Parameters
- num_threadsint, optional
Number of threads to be used for OpenMP parallelization. If None
(default) the value of OMP_NUM_THREADS environment variable is used
if it is set, otherwise all available threads are used. If < 0 the
maximal number of threads minus |num_threads + 1| is used (enter -1
to use as many threads as possible). 0 raises an error. Only
metrics using OpenMP will use this variable.
-
distance(xopt)
Distance calculated from this Metric.
Parameters
- xoptsequence
List of affine parameters as an 1D vector,
-
setup(static, moving)
Setup static and moving sets of streamlines.
Parameters
- staticstreamlines
Fixed or reference set of streamlines.
- movingstreamlines
Moving streamlines.
Notes
Call this after the object is initiated and before distance.
-
class dipy.align.streamlinear.BundleMinDistanceMatrixMetric(num_threads=None)
Bases: StreamlineDistanceMetric
Bundle-based Minimum Distance aka BMD
This is the cost function used by the StreamlineLinearRegistration
Methods
setup(static, moving)
distance(xopt)
Notes
The difference with BundleMinDistanceMetric is that this creates
the entire distance matrix and therefore requires more memory.
-
__init__(num_threads=None)
An abstract class for the metric used for streamline registration.
If the two sets of streamlines match exactly then method distance
of this object should be minimum.
Parameters
- num_threadsint, optional
Number of threads to be used for OpenMP parallelization. If None
(default) the value of OMP_NUM_THREADS environment variable is used
if it is set, otherwise all available threads are used. If < 0 the
maximal number of threads minus |num_threads + 1| is used (enter -1
to use as many threads as possible). 0 raises an error. Only
metrics using OpenMP will use this variable.
-
distance(xopt)
Distance calculated from this Metric.
Parameters
- xoptsequence
List of affine parameters as an 1D vector
-
setup(static, moving)
Setup static and moving sets of streamlines.
Parameters
- staticstreamlines
Fixed or reference set of streamlines.
- movingstreamlines
Moving streamlines.
Notes
Call this after the object is initiated and before distance.
Num_threads is not used in this class. Use BundleMinDistanceMetric
for a faster, threaded and less memory hungry metric
-
class dipy.align.streamlinear.BundleMinDistanceAsymmetricMetric(num_threads=None)
Bases: BundleMinDistanceMetric
Asymmetric Bundle-based Minimum distance.
This is a cost function that can be used by the
StreamlineLinearRegistration class.
-
__init__(num_threads=None)
An abstract class for the metric used for streamline registration.
If the two sets of streamlines match exactly then method distance
of this object should be minimum.
Parameters
- num_threadsint, optional
Number of threads to be used for OpenMP parallelization. If None
(default) the value of OMP_NUM_THREADS environment variable is used
if it is set, otherwise all available threads are used. If < 0 the
maximal number of threads minus |num_threads + 1| is used (enter -1
to use as many threads as possible). 0 raises an error. Only
metrics using OpenMP will use this variable.
-
distance(xopt)
Distance calculated from this Metric.
Parameters
- xoptsequence
List of affine parameters as an 1D vector
-
class dipy.align.streamlinear.BundleSumDistanceMatrixMetric(num_threads=None)
Bases: BundleMinDistanceMatrixMetric
Bundle-based Sum Distance aka BMD
This is a cost function that can be used by the
StreamlineLinearRegistration class.
Methods
setup(static, moving)
distance(xopt)
Notes
The difference with BundleMinDistanceMatrixMetric is that it uses
uses the sum of the distance matrix and not the sum of mins.
-
__init__(num_threads=None)
An abstract class for the metric used for streamline registration.
If the two sets of streamlines match exactly then method distance
of this object should be minimum.
Parameters
- num_threadsint, optional
Number of threads to be used for OpenMP parallelization. If None
(default) the value of OMP_NUM_THREADS environment variable is used
if it is set, otherwise all available threads are used. If < 0 the
maximal number of threads minus |num_threads + 1| is used (enter -1
to use as many threads as possible). 0 raises an error. Only
metrics using OpenMP will use this variable.
-
distance(xopt)
Distance calculated from this Metric
Parameters
- xoptsequence
List of affine parameters as an 1D vector
-
class dipy.align.streamlinear.JointBundleMinDistanceMetric(num_threads=None)
Bases: StreamlineDistanceMetric
Bundle-based Minimum Distance for joint optimization.
This cost function is used by the StreamlineLinearRegistration class when
running halfway streamline linear registration for unbiased groupwise
bundle registration and atlasing.
It computes the BMD distance after moving both static and moving bundles to
a halfway space in between both.
Methods
setup(static, moving)
distance(xopt)
Notes
In this metric both static and moving bundles are treated equally (i.e.,
there is no static reference bundle as both are intended to move). The
naming convention is kept for consistency.
-
__init__(num_threads=None)
An abstract class for the metric used for streamline registration.
If the two sets of streamlines match exactly then method distance
of this object should be minimum.
Parameters
- num_threadsint, optional
Number of threads to be used for OpenMP parallelization. If None
(default) the value of OMP_NUM_THREADS environment variable is used
if it is set, otherwise all available threads are used. If < 0 the
maximal number of threads minus |num_threads + 1| is used (enter -1
to use as many threads as possible). 0 raises an error. Only
metrics using OpenMP will use this variable.
-
distance(xopt)
Distance calculated from this Metric.
Parameters
- xoptsequence
List of affine parameters as an 1D vector. These affine parameters
are used to derive the corresponding halfway transformation
parameters for each bundle.
-
setup(static, moving)
Setup static and moving sets of streamlines.
Parameters
- staticstreamlines
Set of streamlines
- movingstreamlines
Set of streamlines
Notes
Call this after the object is initiated and before distance.
Num_threads is not used in this class.
-
class dipy.align.streamlinear.StreamlineLinearRegistration(metric=None, x0='rigid', method='L-BFGS-B', bounds=None, verbose=False, options=None, evolution=False, num_threads=None)
Bases: object
-
__init__(metric=None, x0='rigid', method='L-BFGS-B', bounds=None, verbose=False, options=None, evolution=False, num_threads=None)
Linear registration of 2 sets of streamlines [Garyfallidis15].
Parameters
- metricStreamlineDistanceMetric,
If None and fast is False then the BMD distance is used. If fast
is True then a faster implementation of BMD is used. Otherwise,
use the given distance metric.
- x0array or int or str
Initial parametrization for the optimization.
- If 1D array with:
a) 6 elements then only rigid registration is performed with
the 3 first elements for translation and 3 for rotation.
b) 7 elements also isotropic scaling is performed (similarity).
c) 12 elements then translation, rotation (in degrees),
scaling and shearing is performed (affine).
Here is an example of x0 with 12 elements:
x0=np.array([0, 10, 0, 40, 0, 0, 2., 1.5, 1, 0.1, -0.5, 0])
This has translation (0, 10, 0), rotation (40, 0, 0) in
degrees, scaling (2., 1.5, 1) and shearing (0.1, -0.5, 0).
- If int:
- 6
x0 = np.array([0, 0, 0, 0, 0, 0])
- 7
x0 = np.array([0, 0, 0, 0, 0, 0, 1.])
- 12
x0 = np.array([0, 0, 0, 0, 0, 0, 1., 1., 1, 0, 0, 0])
- If str:
- “rigid”
x0 = np.array([0, 0, 0, 0, 0, 0])
- “similarity”
x0 = np.array([0, 0, 0, 0, 0, 0, 1.])
- “affine”
x0 = np.array([0, 0, 0, 0, 0, 0, 1., 1., 1, 0, 0, 0])
- methodstr,
‘L_BFGS_B’ or ‘Powell’ optimizers can be used. Default is
‘L_BFGS_B’.
- boundslist of tuples or None,
If method == ‘L_BFGS_B’ then we can use bounded optimization.
For example for the six parameters of rigid rotation we can set
the bounds = [(-30, 30), (-30, 30), (-30, 30),
(-45, 45), (-45, 45), (-45, 45)]
That means that we have set the bounds for the three translations
and three rotation axes (in degrees).
- verbosebool, optional.
If True, if True then information about the optimization is shown.
Default: False.
- optionsNone or dict,
Extra options to be used with the selected method.
- evolutionboolean
If True save the transformation for each iteration of the
optimizer. Default is False. Supported only with Scipy >= 0.11.
- num_threadsint, optional
Number of threads to be used for OpenMP parallelization. If None
(default) the value of OMP_NUM_THREADS environment variable is used
if it is set, otherwise all available threads are used. If < 0 the
maximal number of threads minus |num_threads + 1| is used (enter -1
to use as many threads as possible). 0 raises an error. Only
metrics using OpenMP will use this variable.
References
[Garyfallidis15]
Garyfallidis et al. “Robust and efficient linear
registration of white-matter fascicles in the space of streamlines”,
NeuroImage, 117, 124–140, 2015
[Garyfallidis14]
Garyfallidis et al., “Direct native-space fiber
bundle alignment for group comparisons”, ISMRM, 2014.
[Garyfallidis17]
Garyfallidis et al. Recognition of white matter
bundles using local and global streamline-based
registration and clustering, Neuroimage, 2017.
-
optimize(static, moving, mat=None)
Find the minimum of the provided metric.
Parameters
- staticstreamlines
Reference or fixed set of streamlines.
- movingstreamlines
Moving set of streamlines.
- matarray
Transformation (4, 4) matrix to start the registration. mat
is applied to moving. Default value None which means that initial
transformation will be generated by shifting the centers of moving
and static sets of streamlines to the origin.
Returns
map : StreamlineRegistrationMap
-
class dipy.align.streamlinear.StreamlineRegistrationMap(matopt, xopt, fopt, matopt_history, funcs, iterations)
Bases: object
-
__init__(matopt, xopt, fopt, matopt_history, funcs, iterations)
A map holding the optimum affine matrix and some other parameters
of the optimization
Parameters
- matrixarray,
4x4 affine matrix which transforms the moving to the static
streamlines
- xoptarray,
1d array with the parameters of the transformation after centering
- foptfloat,
final value of the metric
- matrix_historyarray
All transformation matrices created during the optimization
- funcsint,
Number of function evaluations of the optimizer
- iterationsint
Number of iterations of the optimizer
-
transform(moving)
Transform moving streamlines to the static.
Parameters
moving : streamlines
Returns
moved : streamlines
Notes
All this does is apply self.matrix to the input streamlines.
-
class dipy.align.streamlinear.JointStreamlineRegistrationMap(xopt, fopt, matopt_history, funcs, iterations)
Bases: object
-
__init__(xopt, fopt, matopt_history, funcs, iterations)
A map holding the optimum affine matrices for halfway streamline
linear registration and some other parameters of the optimization.
xopt is optimized by StreamlineLinearRegistration using the
JointBundleMinDistanceMetric. In that case the mat argument of the
optimize method needs to be np.eye(4) to avoid streamline centering.
This constructor derives and stores the transformations to move both
static and moving bundles to the halfway space.
Parameters
- xoptarray
1d array with the parameters of the transformation.
- foptfloat
Final value of the metric.
- matopt_historyarray
All transformation matrices created during the optimization.
- funcsint
Number of function evaluations of the optimizer.
- iterationsint
Number of iterations of the optimizer.
-
transform(static, moving)
Transform both static and moving bundles to the halfway space.
All this does is apply self.matrix1 and self.matrix2` to the
static and moving bundles, respectively.
Parameters
static : streamlines
moving : streamlines
Returns
static : streamlines
moving : streamlines
logger
-
dipy.align.streamlinear.logger()
Instances of the Logger class represent a single logging channel. A
“logging channel” indicates an area of an application. Exactly how an
“area” is defined is up to the application developer. Since an
application can have any number of areas, logging channels are identified
by a unique string. Application areas can be nested (e.g. an area
of “input processing” might include sub-areas “read CSV files”, “read
XLS files” and “read Gnumeric files”). To cater for this natural nesting,
channel names are organized into a namespace hierarchy where levels are
separated by periods, much like the Java or Python package namespace. So
in the instance given above, channel names might be “input” for the upper
level, and “input.csv”, “input.xls” and “input.gnu” for the sub-levels.
There is no arbitrary limit to the depth of nesting.
bundle_sum_distance
-
dipy.align.streamlinear.bundle_sum_distance(t, static, moving, num_threads=None)
MDF distance optimization function (SUM).
We minimize the distance between moving streamlines as they align
with the static streamlines.
Parameters
- tndarray
t is a vector of affine transformation parameters with
size at least 6.
If the size is 6, t is interpreted as translation + rotation.
If the size is 7, t is interpreted as translation + rotation +
isotropic scaling.
If size is 12, t is interpreted as translation + rotation +
scaling + shearing.
- staticlist
Static streamlines
- movinglist
Moving streamlines. These will be transformed to align with
the static streamlines
- num_threadsint, optional
Number of threads. If -1 then all available threads will be used.
bundle_min_distance
-
dipy.align.streamlinear.bundle_min_distance(t, static, moving)
MDF-based pairwise distance optimization function (MIN).
We minimize the distance between moving streamlines as they align
with the static streamlines.
Parameters
- tndarray
t is a vector of affine transformation parameters with
size at least 6.
If size is 6, t is interpreted as translation + rotation.
If size is 7, t is interpreted as translation + rotation +
isotropic scaling.
If size is 12, t is interpreted as translation + rotation +
scaling + shearing.
- staticlist
Static streamlines
- movinglist
Moving streamlines.
bundle_min_distance_fast
-
dipy.align.streamlinear.bundle_min_distance_fast(t, static, moving, block_size, num_threads=None)
MDF-based pairwise distance optimization function (MIN).
We minimize the distance between moving streamlines as they align
with the static streamlines.
Parameters
- tarray
1D array. t is a vector of affine transformation parameters with
size at least 6.
If the size is 6, t is interpreted as translation + rotation.
If the size is 7, t is interpreted as translation + rotation +
isotropic scaling.
If size is 12, t is interpreted as translation + rotation +
scaling + shearing.
- staticarray
N*M x 3 array. All the points of the static streamlines. With order of
streamlines intact. Where N is the number of streamlines and M
is the number of points per streamline.
- movingarray
K*M x 3 array. All the points of the moving streamlines. With order of
streamlines intact. Where K is the number of streamlines and M
is the number of points per streamline.
- block_sizeint
Number of points per streamline. All streamlines in static and moving
should have the same number of points M.
- num_threadsint, optional
Number of threads to be used for OpenMP parallelization. If None
(default) the value of OMP_NUM_THREADS environment variable is used
if it is set, otherwise all available threads are used. If < 0 the
maximal number of threads minus |num_threads + 1| is used (enter -1 to
use as many threads as possible). 0 raises an error.
Notes
This is a faster implementation of bundle_min_distance, which requires
that all the points of each streamline are allocated into an ndarray
(of shape N*M by 3, with N the number of points per streamline and M the
number of streamlines). This can be done by calling
dipy.tracking.streamlines.unlist_streamlines.
bundle_min_distance_asymmetric_fast
-
dipy.align.streamlinear.bundle_min_distance_asymmetric_fast(t, static, moving, block_size)
MDF-based pairwise distance optimization function (MIN).
We minimize the distance between moving streamlines as they align
with the static streamlines.
Parameters
- tarray
1D array. t is a vector of affine transformation parameters with
size at least 6.
If the size is 6, t is interpreted as translation + rotation.
If the size is 7, t is interpreted as translation + rotation +
isotropic scaling.
If size is 12, t is interpreted as translation + rotation +
scaling + shearing.
- staticarray
N*M x 3 array. All the points of the static streamlines. With order of
streamlines intact. Where N is the number of streamlines and M
is the number of points per streamline.
- movingarray
K*M x 3 array. All the points of the moving streamlines. With order of
streamlines intact. Where K is the number of streamlines and M
is the number of points per streamline.
- block_sizeint
Number of points per streamline. All streamlines in static and moving
should have the same number of points M.
remove_clusters_by_size
-
dipy.align.streamlinear.remove_clusters_by_size(clusters, min_size=0)
progressive_slr
-
dipy.align.streamlinear.progressive_slr(static, moving, metric, x0, bounds, method='L-BFGS-B', verbose=False, num_threads=None)
Progressive SLR.
This is an utility function that allows for example to do affine
registration using Streamline-based Linear Registration (SLR)
[Garyfallidis15] by starting with translation first, then rigid,
then similarity, scaling and finally affine.
Similarly, if for example, you want to perform rigid then you start with
translation first. This progressive strategy can helps with finding the
optimal parameters of the final transformation.
Parameters
static : Streamlines
moving : Streamlines
metric : StreamlineDistanceMetric
x0 : string
Could be any of ‘translation’, ‘rigid’, ‘similarity’, ‘scaling’,
‘affine’
- boundsarray
Boundaries of registration parameters. See variable DEFAULT_BOUNDS
for example.
- methodstring
L_BFGS_B’ or ‘Powell’ optimizers can be used. Default is ‘L_BFGS_B’.
- verbosebool, optional.
If True, log messages. Default:
- num_threadsint, optional
Number of threads to be used for OpenMP parallelization. If None
(default) the value of OMP_NUM_THREADS environment variable is used
if it is set, otherwise all available threads are used. If < 0 the
maximal number of threads minus |num_threads + 1| is used (enter -1 to
use as many threads as possible). 0 raises an error. Only metrics
using OpenMP will use this variable.
References
[Garyfallidis15]
Garyfallidis et al. “Robust and efficient linear
registration of white-matter fascicles in the space of streamlines”,
NeuroImage, 117, 124–140, 2015
slr_with_qbx
-
dipy.align.streamlinear.slr_with_qbx(static, moving, x0='affine', rm_small_clusters=50, maxiter=100, select_random=None, verbose=False, greater_than=50, less_than=250, qbx_thr=(40, 30, 20, 15), nb_pts=20, progressive=True, rng=None, num_threads=None)
Utility function for registering large tractograms.
For efficiency, we apply the registration on cluster centroids and remove
small clusters.
Parameters
static : Streamlines
moving : Streamlines
- x0str, optional.
rigid, similarity or affine transformation model (default affine)
- rm_small_clustersint, optional
Remove clusters that have less than rm_small_clusters
- maxiterint, optional
Maximum number of iterations to perform.
- select_randomint, optional.
If not, None selects a random number of streamlines to apply clustering
Default None.
- verbosebool, optional
If True, logs information about optimization. Default: False
- greater_thanint, optional
Keep streamlines that have length greater than
this value (default 50)
- less_thanint, optional
Keep streamlines have length less than this value (default 250)
- qbx_thrvariable int
Thresholds for QuickBundlesX (default [40, 30, 20, 15])
- nb_ptsint, optional
Number of points for discretizing each streamline (default 20)
- progressiveboolean, optional
(default True)
- rngRandomState
If None creates RandomState in function.
- num_threadsint, optional
Number of threads to be used for OpenMP parallelization. If None
(default) the value of OMP_NUM_THREADS environment variable is used
if it is set, otherwise all available threads are used. If < 0 the
maximal number of threads minus |num_threads + 1| is used (enter -1 to
use as many threads as possible). 0 raises an error. Only metrics
using OpenMP will use this variable.
Notes
The order of operations is the following. First short or long streamlines
are removed. Second, the tractogram or a random selection of the tractogram
is clustered with QuickBundles. Then SLR [Garyfallidis15] is applied.
References
[Garyfallidis15]
Garyfallidis et al. “Robust and efficient linear
registration of white-matter fascicles in the space of streamlines”,
NeuroImage, 117, 124–140, 2015
.. [Garyfallidis14] Garyfallidis et al., “Direct native-space fiber
bundle alignment for group comparisons”, ISMRM, 2014.
[Garyfallidis17]
Garyfallidis et al. Recognition of white matter
bundles using local and global streamline-based registration and
clustering, Neuroimage, 2017.
groupwise_slr
-
dipy.align.streamlinear.groupwise_slr(bundles, x0='affine', tol=0, max_iter=20, qbx_thr=[4], nb_pts=20, select_random=10000, verbose=False, rng=None)
Function to perform unbiased groupwise bundle registration.
All bundles are moved to the same space by iteratively applying halfway
streamline linear registration in pairs. With each iteration, bundles get
closer to each other until the procedure converges and there is no more
improvement.
Parameters
- bundleslist
List with streamlines of the bundles to be registered.
- x0str, optional
rigid, similarity or affine transformation model. Default: affine.
- tolfloat, optional
Tolerance value to be used to assume convergence. Default: 0.
- max_iterint, optional
Maximum number of iterations. Depending on the number of bundles to be
registered this may need to be larger. Default: 20.
- qbx_thrvariable int, optional
Thresholds for Quickbundles used for clustering streamlines and reduce
computational time. If None, no clustering is performed. Higher values
cluster streamlines into a smaller number of centroids. Default: [4].
- nb_ptsint, optional
Number of points for discretizing each streamline. Default: 20.
- select_randomint, optional
Maximum number of streamlines for each bundle. If None, all the
streamlines are used. Default: 10000.
- verbosebool, optional
If True, logs information. Default: False.
- rngRandomState
If None, creates RandomState in function. Default: None.
References
[Garyfallidis15]
Garyfallidis et al. “Robust and efficient linear
registration of white-matter fascicles in the space of streamlines”,
NeuroImage, 117, 124–140, 2015
.. [Garyfallidis14] Garyfallidis et al., “Direct native-space fiber
bundle alignment for group comparisons”, ISMRM, 2014.
[Garyfallidis17]
Garyfallidis et al. Recognition of white matter
bundles using local and global streamline-based registration and
clustering, Neuroimage, 2017.
get_unique_pairs
-
dipy.align.streamlinear.get_unique_pairs(n_bundle, pairs=None)
Make unique pairs from n_bundle bundles.
The function allows to input a previous pairs assignment so that the new
pairs are different.
Parameters
- n_bundleint
Number of bundles to be matched in pairs.
- pairsarray, optional
array containing the indexes of previous pairs.
compose_matrix44
-
dipy.align.streamlinear.compose_matrix44(t, dtype=<class 'numpy.float64'>)
Compose a 4x4 transformation matrix.
Parameters
- tndarray
This is a 1D vector of affine transformation parameters with
size at least 3.
If the size is 3, t is interpreted as translation.
If the size is 6, t is interpreted as translation + rotation.
If the size is 7, t is interpreted as translation + rotation +
isotropic scaling.
If the size is 9, t is interpreted as translation + rotation +
anisotropic scaling.
If size is 12, t is interpreted as translation + rotation +
scaling + shearing.
Returns
- Tndarray
Homogeneous transformation matrix of size 4x4.
decompose_matrix44
-
dipy.align.streamlinear.decompose_matrix44(mat, size=12)
Given a 4x4 homogeneous matrix return the parameter vector.
Parameters
- matarray
Homogeneous 4x4 transformation matrix
- sizeint
Size of the output vector. 3, for translation, 6 for rigid,
7 for similarity, 9 for scaling and 12 for affine. Default is 12.
Returns
- tndarray
One dimensional ndarray of 3, 6, 7, 9 or 12 affine parameters.
average_bundle_length
-
dipy.align.streamwarp.average_bundle_length(bundle)
Find average Euclidean length of the bundle in mm.
Parameters
- bundleStreamlines
Bundle who’s average length is to be calculated.
Returns
- int
Average Euclidean length of bundle in mm.
find_missing
-
dipy.align.streamwarp.find_missing(lst, cb)
Find unmatched streamline indices in moving bundle.
Parameters
- lstList
List of integers containing all the streamlines indices in moving
bundle.
- cbList
List of integers containing streamline indices of the moving bundle
that were not matched to any streamline in static bundle.
Returns
- list
List containing unmatched streamlines from moving bundle
bundlewarp
-
dipy.align.streamwarp.bundlewarp(static, moving, dist=None, alpha=0.3, beta=20, max_iter=15, affine=True)
Register two bundles using nonlinear method.
Parameters
- staticStreamlines
Reference/fixed bundle
- movingStreamlines
Target bundle that will be moved/registered to match the static bundle
- distfloat, optional.
Precomputed distance matrix (default None)
- alphafloat, optional
Represents the trade-off between regularizing the deformation and
having points match very closely. Lower value of alpha means high
deformations (default 0.3)
- betaint, optional
Represents the strength of the interaction between points
Gaussian kernel size (default 20)
- max_iterint, optional
Maximum number of iterations for deformation process in ml-CPD method
(default 15)
- affineboolean, optional
If False, use rigid registration as starting point (default True)
Returns
- deformed_bundleStreamlines
Nonlinearly moved bundle (warped bundle)
- moving_alignedStreamlines
Linearly moved bundle (affinely moved)
- distnp.ndarray
Float array containing distance between moving and static bundle
- matched_pairsnp.ndarray
Int array containing streamline correspondences between two bundles
- warpnp.ndarray
Nonlinear warp map generated by BundleWarp
References
[Chandio2023]
Chandio et al. “BundleWarp, streamline-based nonlinear
registration of white matter tracts.” bioRxiv (2023): 2023-01.
bundlewarp_vector_filed
-
dipy.align.streamwarp.bundlewarp_vector_filed(moving_aligned, deformed_bundle)
Calculate vector fields.
Vector field computation as the difference between each streamline point
in the deformed and linearly aligned bundles
Parameters
- moving_alignedStreamlines
Linearly (affinely) moved bundle
- deformed_bundleStreamlines
Nonlinearly (warped) bundle
Returns
- offsetsList
Vector field modules
- directionsList
Unitary vector directions
colors : List
bundlewarp_shape_analysis
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dipy.align.streamwarp.bundlewarp_shape_analysis(moving_aligned, deformed_bundle, no_disks=10, plotting=False)
Calculate bundle shape difference profile.
Bundle shape difference analysis using magnitude from BundleWarp
displacements and BUAN
Parameters
- moving_alignedStreamlines
Linearly (affinely) moved bundle
- deformed_bundleStreamlines
Nonlinearly (warped) bundle
- no_disksint
Number of segments to be created along the length of the bundle
(Default 10)
- plottingBoolean, optional
Plot bundle shape profile (default False)
Returns
- shape_profilennp.ndarray
Float array containing bundlewarp displacement magnitudes along the
length of the bundle
- stdvnp.ndarray
Float array containing standard deviations