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registration.py
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registration.py
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import itk
import itk.itkAffineTransformPython
import itk.itkNormalVariateGeneratorPython
import itk.itkOnePlusOneEvolutionaryOptimizerv4Python
import itk.itkTranslationTransformPython
from observer import CommandIterationUpdate
from itk.support.types import PixelTypes
def register_images(fixed_path, moving_path, save_path):
print("Beginning of the registration")
PixelType = itk.D
fixed_image = itk.imread(fixed_path, PixelType)
moving_image = itk.imread(moving_path, PixelType)
dimension = fixed_image.GetImageDimension()
FixedImageType = type(fixed_image)
MovingImageType = type(moving_image)
TransformType = itk.TranslationTransform[itk.D, dimension]
initial_transform = TransformType.New()
initial_transform.SetIdentity()
# This paramaters were found by grid search to get optimal results
optimizer = itk.RegularStepGradientDescentOptimizerv4.New()
optimizer.SetLearningRate(1.0)
optimizer.SetMinimumStepLength(0.01)
optimizer.SetMaximumStepSizeInPhysicalUnits(0.01)
optimizer.SetNumberOfIterations(200)
optimizer.SetRelaxationFactor(0.5)
# Uncomment this line to have the observer
# iteration_update = CommandIterationUpdate(optimizer)
image1_interpolation = itk.LinearInterpolateImageFunction[
FixedImageType, itk.D
].New()
metric = itk.MeanSquaresImageToImageMetricv4[FixedImageType, MovingImageType].New()
metric.SetFixedInterpolator(image1_interpolation)
metric2 = itk.CorrelationImageToImageMetricv4[FixedImageType, MovingImageType].New()
metric2.SetFixedInterpolator(image1_interpolation)
metric3 = itk.MattesMutualInformationImageToImageMetricv4[
FixedImageType, MovingImageType
].New()
metric3.SetFixedInterpolator(image1_interpolation)
registration = itk.ImageRegistrationMethodv4[FixedImageType, MovingImageType].New(
FixedImage=fixed_image,
MovingImage=moving_image,
Metric=metric,
Optimizer=optimizer,
InitialTransform=initial_transform,
)
# Set moving initial transform to identity
moving_initial_transform = TransformType.New()
moving_initial_transform.SetIdentity()
registration.SetMovingInitialTransform(moving_initial_transform)
registration.SetNumberOfLevels(3)
registration.SetSmoothingSigmasPerLevel([4, 2, 1])
registration.SetShrinkFactorsPerLevel([4, 2, 1])
moving_initial_transform = TransformType.New()
initial_parameters = moving_initial_transform.GetParameters()
initial_parameters[0] = 0
initial_parameters[1] = 0
moving_initial_transform.SetParameters(initial_parameters)
registration.SetMovingInitialTransform(moving_initial_transform)
# identity_transform = TransformType.New()
# identity_transform.SetIdentity()
# registration.SetFixedInitialTransform(identity_transform)
registration.Update()
res_transform = registration.GetTransform()
final_parameters = res_transform.GetParameters()
translation_along_x = final_parameters.GetElement(0)
translation_along_y = final_parameters.GetElement(1)
translation_along_z = final_parameters.GetElement(2)
number_of_iterations = optimizer.GetCurrentIteration()
best_value = optimizer.GetValue()
# print("Result = ")
# print(" Translation X = " + str(translation_along_x))
# print(" Translation Y = " + str(translation_along_y))
# print(" Translation Z = " + str(translation_along_z))
# print(" Iterations = " + str(number_of_iterations))
# print(" Metric value = " + str(best_value))
CompositeTransformType = itk.CompositeTransform[itk.D, dimension]
output_composite_transform = CompositeTransformType.New()
output_composite_transform.AddTransform(moving_initial_transform)
output_composite_transform.AddTransform(registration.GetModifiableTransform())
resample = itk.ResampleImageFilter.New(
Input=moving_image,
Transform=output_composite_transform,
UseReferenceImage=True,
ReferenceImage=fixed_image,
Interpolator=image1_interpolation,
)
resample.SetDefaultPixelValue(130)
resample.Update()
resampled_image = resample.GetOutput()
itk.imwrite(resampled_image, save_path)
print("Registration has ended")
def register_images_optimizer(fixed_path, moving_path, save_path):
PixelType = itk.D
fixed_image = itk.imread(fixed_path, PixelType)
moving_image = itk.imread(moving_path, PixelType)
dimension = fixed_image.GetImageDimension()
FixedImageType = type(fixed_image)
MovingImageType = type(moving_image)
TransformType = itk.TranslationTransform[itk.D, dimension]
initial_transform = TransformType.New()
initial_transform.SetIdentity()
optimizer = itk.RegularStepGradientDescentOptimizerv4.New()
optimizer.SetLearningRate(0.5)
optimizer.SetMinimumStepLength(0.001)
optimizer.SetMaximumStepSizeInPhysicalUnits(0.1)
optimizer.SetNumberOfIterations(200)
optimizer.SetRelaxationFactor(0.5)
iteration_update = CommandIterationUpdate(optimizer)
image1_interpolation = itk.LinearInterpolateImageFunction[
FixedImageType, itk.D
].New()
metric = itk.MeanSquaresImageToImageMetricv4[FixedImageType, MovingImageType].New()
metric.SetFixedInterpolator(image1_interpolation)
metric2 = itk.CorrelationImageToImageMetricv4[FixedImageType, MovingImageType].New()
metric2.SetFixedInterpolator(image1_interpolation)
metric3 = itk.MattesMutualInformationImageToImageMetricv4[
FixedImageType, MovingImageType
].New()
metric3.SetFixedInterpolator(image1_interpolation)
registration = itk.ImageRegistrationMethodv4[FixedImageType, MovingImageType].New(
FixedImage=fixed_image,
MovingImage=moving_image,
Metric=metric,
Optimizer=optimizer,
InitialTransform=initial_transform,
)
# Set moving initial transform to identity
moving_initial_transform = TransformType.New()
moving_initial_transform.SetIdentity()
registration.SetMovingInitialTransform(moving_initial_transform)
registration.SetNumberOfLevels(3)
registration.SetSmoothingSigmasPerLevel([4, 2, 1])
registration.SetShrinkFactorsPerLevel([4, 2, 1])
moving_initial_transform = TransformType.New()
initial_parameters = moving_initial_transform.GetParameters()
initial_parameters[0] = 0
initial_parameters[1] = 0
moving_initial_transform.SetParameters(initial_parameters)
registration.SetMovingInitialTransform(moving_initial_transform)
# identity_transform = TransformType.New()
# identity_transform.SetIdentity()
# registration.SetFixedInitialTransform(identity_transform)
registration.Update()
res_transform = registration.GetTransform()
final_parameters = res_transform.GetParameters()
translation_along_x = final_parameters.GetElement(0)
translation_along_y = final_parameters.GetElement(1)
translation_along_z = final_parameters.GetElement(2)
number_of_iterations = optimizer.GetCurrentIteration()
best_value = optimizer.GetValue()
print("Result = ")
print(" Translation X = " + str(translation_along_x))
print(" Translation Y = " + str(translation_along_y))
print(" Translation Z = " + str(translation_along_z))
print(" Iterations = " + str(number_of_iterations))
print(" Metric value = " + str(best_value))
CompositeTransformType = itk.CompositeTransform[itk.D, dimension]
output_composite_transform = CompositeTransformType.New()
output_composite_transform.AddTransform(moving_initial_transform)
output_composite_transform.AddTransform(registration.GetModifiableTransform())
resample = itk.ResampleImageFilter.New(
Input=moving_image,
Transform=output_composite_transform,
UseReferenceImage=True,
ReferenceImage=fixed_image,
Interpolator=image1_interpolation,
)
resample.SetDefaultPixelValue(130)
resample.Update()
resampled_image = resample.GetOutput()
itk.imwrite(resampled_image, save_path)
def registerEvolutionary(fixed_path, moving_path, save_path):
PixelType = itk.D
fixed_image = itk.imread(fixed_path, PixelType)
moving_image = itk.imread(moving_path, PixelType)
dimension = fixed_image.GetImageDimension()
FixedImageType = type(fixed_image)
MovingImageType = type(moving_image)
image1_interpolation = itk.LinearInterpolateImageFunction[
FixedImageType, itk.D
].New()
metric = itk.MattesMutualInformationImageToImageMetricv4[
FixedImageType, MovingImageType
].New()
metric.SetFixedInterpolator(image1_interpolation)
TransformType = itk.AffineTransform[itk.D, dimension]
initial_transform = TransformType.New()
initial_transform.SetIdentity()
metric.SetNumberOfHistogramBins(60)
generator = itk.itkNormalVariateGeneratorPython.itkNormalVariateGenerator_New()
generator.Initialize(12345)
optimizer = (
itk.itkOnePlusOneEvolutionaryOptimizerv4Python.itkOnePlusOneEvolutionaryOptimizerv4D_New()
)
optimizer.SetNormalVariateGenerator(generator)
optimizer.Initialize(15)
optimizer.SetEpsilon(3.0)
optimizer.SetMaximumIteration(400)
iteration_update = CommandIterationUpdate(optimizer)
registration = itk.ImageRegistrationMethodv4[FixedImageType, MovingImageType].New(
FixedImage=fixed_image,
MovingImage=moving_image,
Metric=metric,
Optimizer=optimizer,
InitialTransform=initial_transform,
)
moving_initial_transform = TransformType.New()
moving_initial_transform.SetIdentity()
registration.SetMovingInitialTransform(moving_initial_transform)
registration.SetNumberOfLevels(3)
registration.SetSmoothingSigmasPerLevel([4, 2, 1])
registration.SetShrinkFactorsPerLevel([4, 2, 1])
registration.SamplingStrategy = "Random"
registration.SamplingPercentage = 0.1
registration.Update()
res_transform = registration.GetTransform()
final_parameters = res_transform.GetParameters()
translation_along_x = final_parameters.GetElement(0)
translation_along_y = final_parameters.GetElement(1)
translation_along_z = final_parameters.GetElement(2)
number_of_iterations = optimizer.GetCurrentIteration()
best_value = optimizer.GetValue()
print("Result = ")
print(" Translation X = " + str(translation_along_x))
print(" Translation Y = " + str(translation_along_y))
print(" Translation Z = " + str(translation_along_z))
print(" Iterations = " + str(number_of_iterations))
print(" Metric value = " + str(best_value))
# CompositeTransformType = itk.CompositeTransform[itk.D, dimension]
# output_composite_transform = CompositeTransformType.New()
# output_composite_transform.AddTransform(moving_initial_transform)
# output_composite_transform.AddTransform(registration.GetModifiableTransform())
resample = itk.ResampleImageFilter.New(
Input=moving_image,
Transform=res_transform,
UseReferenceImage=True,
ReferenceImage=fixed_image,
Interpolator=image1_interpolation,
)
resample.SetDefaultPixelValue(130)
resample.Update()
resampled_image = resample.GetOutput()
itk.imwrite(resampled_image, save_path)
mse_calculator = itk.MeanSquaresImageToImageMetricv4[
FixedImageType, MovingImageType
].New()
mse_calculator.SetFixedImage(fixed_image)
mse_calculator.SetMovingImage(moving_image)
mse_calculator.SetFixedInterpolator(image1_interpolation)
mse_calculator.SetMovingInterpolator(image1_interpolation)
mse_calculator.SetTransform(res_transform)
mse_calculator.Initialize()
print("MSE Value:", mse_calculator.GetValue())
if __name__ == "__main__":
fixed = "Data/case6_gre1.nrrd"
moving = "Data/case6_gre2.nrrd"
save = "registered.nrrd"
register_images(fixed, moving, save)