def itk_registration_rigid_3d(fixed_image, moving_image, options):
"""
A Python implementation for a Rigid Body 3D registration, utilizing
ITK (www.itk.org) library functions.
:param fixed_image: The reference image. Must be an instance of
sitk.Image class.
:param moving_image: The image for which the spatial transform will
be calculated. Same requirements as above.
:param options: Options provided by the user via CLI or the
included GUI. See image_quality_options.py.
:return:
The final transform as a sitk.Euler2DTransform
"""
print('Setting up registration job')
assert isinstance(fixed_image, sitk.Image)
assert isinstance(moving_image, sitk.Image)
fixed_image = sitk.Cast(fixed_image, sitk.sitkFloat32)
moving_image = sitk.Cast(moving_image, sitk.sitkFloat32)
# REGISTRATION COMPONENTS SETUP
# ========================================================================
registration = sitk.ImageRegistrationMethod()
# OPTIMIZER
registration.SetOptimizerAsRegularStepGradientDescent(
options.learning_rate,
options.min_step_length,
options.registration_max_iterations,
relaxationFactor=options.relaxation_factor,
estimateLearningRate=registration.EachIteration)
registration.SetOptimizerScalesFromJacobian()
# translation_scale = 1.0/options.translation_scale
# registration.SetOptimizerScales([1.0, translation_scale, translation_scale])
# INTERPOLATOR
registration.SetInterpolator(sitk.sitkLinear)
# METRIC
if options.registration_method == 'mattes':
registration.SetMetricAsMattesMutualInformation(
numberOfHistogramBins=options.mattes_histogram_bins)
elif options.registration_method == 'correlation':
registration.SetMetricAsCorrelation()
elif options.registration_method == 'mean-squared-difference':
registration.SetMetricAsMeanSquares()
else:
raise ValueError("Unknown metric: %s" % options.registration_method)
registration.SetMetricSamplingStrategy(registration.RANDOM)
registration.SetMetricSamplingPercentage(options.sampling_percentage)
if options.reg_translate_only:
tx = sitk.TranslationTransform(3)
else:
tx = sitk.Euler3DTransform()
transform = sitk.CenteredTransformInitializer(
fixed_image, moving_image, tx,
sitk.CenteredTransformInitializerFilter.MOMENTS)
registration.SetInitialTransform(transform)
tx.SetCenter(ops_itk.calculate_center_of_image(moving_image))
registration.SetInitialTransform(tx)
if options.reg_enable_observers:
# OBSERVERS
registration.AddCommand(sitk.sitkStartEvent, start_plot)
registration.AddCommand(sitk.sitkIterationEvent,
lambda: plot_values(registration))
# START
# ========================================================================
print("Starting registration")
final_transform = registration.Execute(fixed_image, moving_image)
print(('Final metric value: {0}'.format(registration.GetMetricValue())))
print(('Optimizer\'s stopping condition, {0}'.format(
registration.GetOptimizerStopConditionDescription())))
if options.reg_enable_observers:
end_plot(fixed_image, moving_image, final_transform)
return final_transform
def itk_registration_similarity_2d(fixed_image, moving_image, options):
"""
A Python implementation for a Rigid Body 2D registration, utilizing
ITK (www.itk.org) library functions.
:param fixed_image: The reference image. Must be an instance of
sitk.Image class.
:param moving_image: The image that is to be registered. Must be
an instance of sitk.Image class.
The image for which the spatial transform will
be calculated. Same requirements as above.
:param options: Options provided by the user via CLI
:return: The final transform as a sitk.Similarity2DTransform
"""
print('Setting up registration job')
assert isinstance(fixed_image, sitk.Image)
assert isinstance(moving_image, sitk.Image)
fixed_image = sitk.Cast(fixed_image, sitk.sitkFloat32)
moving_image = sitk.Cast(moving_image, sitk.sitkFloat32)
# REGISTRATION COMPONENTS SETUP
# ========================================================================
registration = sitk.ImageRegistrationMethod()
# OPTIMIZER
registration.SetOptimizerAsRegularStepGradientDescent(
options.max_step_length,
options.min_step_length,
options.registration_max_iterations,
relaxationFactor=options.relaxation_factor)
translation_scale = 1.0 / options.translation_scale
scaling_scale = 1.0 / options.scaling_scale
registration.SetOptimizerScales(
[scaling_scale, 1.0, translation_scale, translation_scale])
# INTERPOLATOR
registration.SetInterpolator(sitk.sitkLinear)
# METRIC
if options.registration_method == 'mattes':
registration.SetMetricAsMattesMutualInformation(
numberOfHistogramBins=options.mattes_histogram_bins)
registration.SetMetricSamplingStrategy(registration.RANDOM)
registration.SetMetricSamplingPercentage(
options.mattes_sampling_percentage)
elif options.registration_method == 'correlation':
registration.SetMetricAsCorrelation()
elif options.registration_method == 'mean-squared-difference':
registration.SetMetricAsMeanSquares()
else:
raise ValueError("Unknown metric: %s" % options.registration_method)
print('Calculating initial registration parameters')
tx = sitk.Similarity2DTransform()
tx.SetAngle(options.set_rotation)
tx.SetScale(options.set_scale)
if options.initializer:
transform = sitk.CenteredTransformInitializer(
fixed_image, moving_image, tx,
sitk.CenteredTransformInitializerFilter.GEOMETRY)
registration.SetInitialTransform(transform)
else:
tx.SetTranslation([options.y_offset, options.x_offset])
tx.SetCenter(ops_itk.calculate_center_of_image(moving_image))
registration.SetInitialTransform(tx)
# OBSERVERS
registration.AddCommand(sitk.sitkStartEvent, start_plot)
#registration.AddCommand(sitk.sitkEndEvent, end_plot)
registration.AddCommand(sitk.sitkIterationEvent,
lambda: plot_values(registration))
# START
# ========================================================================
print("Starting registration")
final_transform = registration.Execute(fixed_image, moving_image)
print(('Final metric value: {0}'.format(registration.GetMetricValue())))
print(('Optimizer\'s stopping condition, {0}'.format(
registration.GetOptimizerStopConditionDescription())))
end_plot(fixed_image, moving_image, final_transform)
return final_transform