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patientAffineRegistration.py
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import os
import numpy as np
from tqdm import tqdm
import SimpleITK as sitk
import matplotlib.pyplot as plt
import operator
import statistics
import collections
import pickle
import time
from viewer import *
class PatientAffineRegistration:
def __init__(self, segmented_images_arrays):
self.segmented_images_arrays = segmented_images_arrays
self.before_segmented_images_arrays = segmented_images_arrays[0]
self.after_segmented_images_arrays = segmented_images_arrays[1]
self.multires_iterations = []
self.metric_values = []
def save_registration_steps(self, folder_path, registration_method, slice_number):
global metric_values, multires_iterations
metric_values.append(registration_method.GetMetricValue())
plt.plot(metric_values, 'r')
plt.xlabel('Iteration Number', fontsize=12)
plt.ylabel('Metric Value', fontsize=12)
plt.savefig(folder_path + "metric_values\\" + "metric_val_" + str(slice_number) + ".png")
plt.close()
global iteration_number
print(iteration_number)
plt.close('all')
iteration_number += 1
def start_plot(self):
global metric_values, multires_iterations
metric_values = []
multires_iterations = []
self.metric_values = []
self.multires_iterations = []
def end_plot(self):
global metric_values, multires_iterations
del metric_values
del multires_iterations
self.metric_values = None
self.multires_iterations = None
plt.close()
def update_multires_iterations(self):
self.multires_iterations.append(len(self.metric_values))
def save_one_step_images(self, fixed, moving, transform, folder, slice_number):
moving_transformed = sitk.Resample(moving, fixed, transform,
sitk.sitkLinear, 0.0,
moving.GetPixelIDValue())
show_overlayed_images(moving_transformed, fixed, "affine_final_overlayed_slice_" + str(slice_number),
folder)
def affine_registration(self):
save_information = False
folder_path = 'patient\\affine_registration_fixed\\'
final_transforms = []
moving_resampled_images = []
global iteration_number
iteration_number = 0
with tqdm(total=len(self.after_segmented_images_arrays), desc="registration") as registration_bar:
for index, slices in enumerate(zip(self.before_segmented_images_arrays, self.after_segmented_images_arrays)):
before_image = sitk.GetImageFromArray(slices[0])
after_image = sitk.GetImageFromArray(slices[1])
before_slice_image = sitk.Cast(before_image, sitk.sitkFloat32)
after_slice_image = sitk.Cast(after_image, sitk.sitkFloat32)
initial_transform = sitk.CenteredTransformInitializer(before_slice_image,
after_slice_image,
sitk.AffineTransform(before_slice_image.GetDimension()),
sitk.CenteredTransformInitializerFilter.GEOMETRY)
moving_resampled = sitk.Resample(after_slice_image, before_slice_image, initial_transform, sitk.sitkLinear,
0.0, after_slice_image.GetPixelID())
show_overlayed_images(moving_resampled, before_slice_image, "overlayed_before_affine_registration_" + str(index),
folder_path+"initial_transforms\\")
registration_method = sitk.ImageRegistrationMethod()
registration_method.SetMetricAsMattesMutualInformation(numberOfHistogramBins=50)
registration_method.SetMetricSamplingStrategy(registration_method.REGULAR)
registration_method.SetMetricSamplingPercentage(1)
registration_method.SetInterpolator(sitk.sitkLinear)
# registration_method.SetOptimizerAsGradientDescent(learningRate=2, numberOfIterations=50, convergenceMinimumValue=1e-4, convergenceWindowSize=5)
# registration_method.SetOptimizerAsGradientDescent(learningRate=2, numberOfIterations=100, convergenceMinimumValue=1e-5, convergenceWindowSize=10)
registration_method.SetOptimizerAsGradientDescent(learningRate=1, numberOfIterations=255)
registration_method.SetOptimizerScalesFromPhysicalShift()
registration_method.SetShrinkFactorsPerLevel(shrinkFactors = [6,4,2,1])
registration_method.SetSmoothingSigmasPerLevel(smoothingSigmas=[3,2,1,0])
registration_method.SmoothingSigmasAreSpecifiedInPhysicalUnitsOn()
registration_method.SetInitialTransform(initial_transform)
if(save_information):
registration_method.AddCommand(sitk.sitkStartEvent, self.start_plot)
registration_method.AddCommand(sitk.sitkEndEvent, self.end_plot)
registration_method.AddCommand(sitk.sitkMultiResolutionIterationEvent, self.update_multires_iterations())
registration_method.AddCommand(sitk.sitkIterationEvent, lambda: self.save_registration_steps(folder_path, registration_method, index))
registration_method.AddCommand(sitk.sitkIterationEvent, lambda: self.save_one_step_images(before_slice_image,
after_slice_image,
initial_transform,
folder_path+"transformed_images\\",
index))
# start_patient_affine_time = time.time()
before_slice_cast = sitk.Cast(before_slice_image, sitk.sitkFloat32)
after_slice_cast = sitk.Cast(after_slice_image, sitk.sitkFloat32)
final_transform = registration_method.Execute(before_slice_cast, after_slice_cast)
# end_patient_affine_time = time.time()
# patient_affine_time = end_patient_affine_time - start_patient_affine_time
# print('Total affine 2D registration time: ' + str(patient_affine_time))
# print('End metric value: {0}'.format(registration_method.GetMetricValue()))
# print('Optimizer\'s stopping condition, {0}'.format(
# registration_method.GetOptimizerStopConditionDescription()))
moving_resampled = sitk.Resample(after_slice_image, before_slice_image, final_transform, sitk.sitkLinear, 0.0, after_slice_image.GetPixelID())
# np.save(folder_path+"moving_resampled_arrays\\moving_resampled_array_" + str(index), sitk.GetArrayFromImage(moving_resampled))
# sitk.WriteTransform(final_transform, folder_path+"transformations\\" + "affine_transform_"+str(index)+".tfm")
final_transforms.append(final_transform)
moving_resampled_images.append(sitk.GetArrayFromImage(moving_resampled))
registration_bar.update(1)
return [final_transforms, moving_resampled_images]