Source code for antspynet.utilities.randomly_transform_image_data
import ants
import numpy as np
[docs]def randomly_transform_image_data(reference_image,
input_image_list,
segmentation_image_list=None,
number_of_simulations=10,
transform_type='affine',
sd_affine=0.02,
deformation_transform_type="bspline",
number_of_random_points=1000,
sd_noise=10.0,
number_of_fitting_levels=4,
mesh_size=1,
sd_smoothing=4.0,
input_image_interpolator='linear',
segmentation_image_interpolator='nearestNeighbor'):
"""
Randomly transform image data (optional: with corresponding segmentations).
Apply rigid, affine and/or deformable maps to an input set of training
images. The reference image domain defines the space in which this
happens.
Arguments
---------
reference_image : ANTsImage
Defines the spatial domain for all output images. If the input images do
not match the spatial domain of the reference image, we internally
resample the target to the reference image. This could have unexpected
consequences. Resampling to the reference domain is performed by testing
using ants.image_physical_space_consistency then calling
ants.resample_image_to_target with failure.
input_image_list : list of lists of ANTsImages
List of lists of input images to warp. The internal list sets contain one
or more images (per subject) which are assumed to be mutually aligned. The
outer list contains multiple subject lists which are randomly sampled to
produce output image list.
segmentation_image_list : list of ANTsImages
List of segmentation images corresponding to the input image list (optional).
number_of_simulations : integer
Number of simulated output image sets.
transform_type : string
One of the following options: "translation", "rigid", "scaleShear", "affine",
"deformation", "affineAndDeformation".
sd_affine : float
Parameter dictating deviation amount from identity for random linear
transformations.
deformation_transform_type : string
"bspline" or "exponential".
number_of_random_points : integer
Number of displacement points for the deformation field.
sd_noise : float
Standard deviation of the displacement field.
number_of_fitting_levels : integer
Number of fitting levels (bspline deformation only).
mesh_size : int or n-D tuple
Determines fitting resolution (bspline deformation only).
sd_smoothing : float
Standard deviation of the Gaussian smoothing in mm (exponential field only).
input_image_interpolator : string
One of the following options: "nearestNeighbor", "linear", "gaussian", "bSpline".
segmentation_image_interpolator : string
Only "nearestNeighbor" is currently available.
Returns
-------
list of lists of transformed images
Example
-------
>>> import ants
>>> image1_list = list()
>>> image1_list.append(ants.image_read(ants.get_ants_data("r16")))
>>> image2_list = list()
>>> image2_list.append(ants.image_read(ants.get_ants_data("r64")))
>>> input_segmentations = list()
>>> input_segmentations.append(ants.threshold_image(image1, "Otsu", 3))
>>> input_segmentations.append(ants.threshold_image(image2, "Otsu", 3))
>>> input_images = list()
>>> input_images.append(image1_list)
>>> input_images.append(image2_list)
>>> data = antspynet.randomly_transform_image_data(image1,
>>> input_images, input_segmentations, sd_affine=0.02,
>>> transform_type = "affineAndDeformation" )
"""
def polar_decomposition(X):
U, d, V = np.linalg.svd(X, full_matrices=False)
P = np.matmul(U, np.matmul(np.diag(d), np.transpose(U)))
Z = np.matmul(U, V)
if np.linalg.det(Z) < 0:
n = X.shape[0]
reflection_matrix = np.identity(n)
reflection_matrix[0,0] = -1.0
Z = np.matmul(Z, reflection_matrix)
return({"P" : P, "Z" : Z, "Xtilde" : np.matmul(P, Z)})
def create_random_linear_transform(image,
fixed_parameters,
transform_type='affine',
sd_affine=0.02):
transform = ants.create_ants_transform(transform_type=
"AffineTransform", precision='float', dimension=image.dimension)
ants.set_ants_transform_fixed_parameters(transform, fixed_parameters)
identity_parameters = ants.get_ants_transform_parameters(transform)
random_epsilon = np.random.normal(loc=0, scale=sd_affine, size=len(identity_parameters))
if transform_type == 'translation':
random_epsilon[:(len(identity_parameters) - image.dimension)] = 0
random_parameters = identity_parameters + random_epsilon
random_matrix = np.reshape(
random_parameters[:(len(identity_parameters) - image.dimension)],
newshape=(image.dimension, image.dimension))
decomposition = polar_decomposition(random_matrix)
if transform_type == "rigid":
random_matrix = decomposition['Z']
elif transform_type == "affine":
random_matrix = decomposition['Xtilde']
elif transform_type == "scaleShear":
random_matrix = decomposition['P']
random_parameters[:(len(identity_parameters) - image.dimension)] = \
np.reshape(random_matrix, newshape=(len(identity_parameters) - image.dimension))
ants.set_ants_transform_parameters(transform, random_parameters)
return(transform)
def create_random_displacement_field_transform(image,
field_type="bspline",
number_of_random_points=1000,
sd_noise=10.0,
number_of_fitting_levels=4,
mesh_size=1,
sd_smoothing=4.0):
displacement_field = ants.simulate_displacement_field(image,
field_type=field_type, number_of_random_points=number_of_random_points,
sd_noise=sd_noise, enforce_stationary_boundary=True,
number_of_fitting_levels=number_of_fitting_levels, mesh_size=mesh_size,
sd_smoothing=sd_smoothing)
return(ants.transform_from_displacement_field(displacement_field))
admissible_transforms = ("translation", "rigid", "scaleShear", "affine",
"affineAndDeformation", "deformation")
if not transform_type in admissible_transforms:
raise ValueError("The specified transform is not a possible option. Please see help menu.")
# Get the fixed parameters from the reference image.
fixed_parameters = ants.get_center_of_mass(reference_image)
number_of_subjects = len(input_image_list)
random_indices = np.random.choice(number_of_subjects, size=number_of_simulations, replace=True)
simulated_image_list = list()
simulated_segmentation_image_list = list()
simulated_transforms = list()
for i in range(number_of_simulations):
single_subject_image_list = input_image_list[random_indices[i]]
single_subject_segmentation_image = None
if segmentation_image_list is not None:
single_subject_segmentation_image = segmentation_image_list[random_indices[i]]
if ants.image_physical_space_consistency(reference_image, single_subject_image_list[0]) is False:
for j in range(len(single_subject_image_list)):
single_subject_image_list.append(
ants.resample_image_to_target(single_subject_image_list[j], reference_image,
interp_type=input_image_interpolator))
if single_subject_segmentation_image is not None:
single_subject_segmentation_image = \
ants.resample_image_to_target(single_subject_segmentation_image, reference_image,
interp_type=segmentation_image_interpolator)
transforms = list()
if transform_type == 'deformation':
deformable_transform = create_random_displacement_field_transform(
reference_image, deformation_transform_type, number_of_random_points,
sd_noise, number_of_fitting_levels, mesh_size, sd_smoothing)
transforms.append(deformable_transform)
elif transform_type == 'affineAndDeformation':
deformable_transform = create_random_displacement_field_transform(
reference_image, deformation_transform_type, number_of_random_points,
sd_noise, number_of_fitting_levels, mesh_size, sd_smoothing)
linear_transform = create_random_linear_transform(reference_image,
fixed_parameters, 'affine', sd_affine)
transforms.append(deformable_transform)
transforms.append(linear_transform)
else:
linear_transform = create_random_linear_transform(reference_image,
fixed_parameters, transform_type, sd_affine)
transforms.append(linear_transform)
simulated_transforms.append(ants.compose_ants_transforms(transforms))
single_subject_simulated_image_list = list()
for j in range(len(single_subject_image_list)):
single_subject_image = single_subject_image_list[j]
single_subject_simulated_image_list.append(ants.apply_ants_transform_to_image(
simulated_transforms[i], single_subject_image,
reference=reference_image, interpolation=input_image_interpolator.lower()))
simulated_image_list.append(single_subject_simulated_image_list)
if single_subject_segmentation_image is not None:
simulated_segmentation_image_list.append(ants.apply_ants_transform_to_image(
simulated_transforms[i], single_subject_segmentation_image,
reference=reference_image, interpolation=segmentation_image_interpolator.lower()))
if segmentation_image_list is None:
return({'simulated_images' : simulated_image_list,
'simulated_transforms' : simulated_transforms,
'which_subject' : random_indices})
else:
return({'simulated_images' : simulated_image_list,
'simulated_segmentation_images' : simulated_segmentation_image_list,
'simulated_transforms' : simulated_transforms,
'which_subject' : random_indices})