def BSplineOnly(f, m, p, path, elastix_path, output, plot=False):
"Peforms BSpline transformation, using the fixed image, moving image, provided parameter file"
E = elastix.ElastixInterface(elastix_path)
E.register(fixed_image=f,
moving_image=m,
parameters=[p],
output_dir=output)
if plot == True:
for i in range(4):
L = elastix.logfile('{}\IterationInfo.0.R{}.txt'.format(path, i))
# Plot the 'metric' against the iteration number 'itnr'
plt.figure()
plt.plot(L['itnr'], L['metric'])
plt.tight_layout()
plt.xlabel('Iteration Number', size=16)
plt.ylabel('Mattes Mutual Information', size=16)
plt.xticks(size=14)
plt.yticks(size=14)
transform_path0 = os.path.join(result_path, 'TransformParameters.0.txt')
transform_path1 = os.path.join(result_path, 'TransformParameters.1.txt')
final_transform_path = os.path.join(result_path, 'transform_pathfinal.txt')
# Change FinalBSplineInterpolationOrder to 0 for binary mask transformation
TransformParameterFileEditor(transform_path1, transform_path0, final_transform_path).modify_transform_parameter_file()
# Make a new transformix object tr with the CORRECT PATH to transformix
tr = elastix.TransformixInterface(parameters=final_transform_path,
transformix_path=TRANSFORMIX_PATH)
transformed_pr_path = tr.transform_image(pr_image_path, output_dir=result_path)
image_array_tpr = sitk.GetArrayFromImage(sitk.ReadImage(transformed_pr_path))
log_path = os.path.join(result_path, 'IterationInfo.1.R3.txt')
log = elastix.logfile(log_path)
DSC.append(dice_coef(image_array_opr, image_array_tpr))
SNS.append(sensitivity(image_array_opr, image_array_tpr))
SPC.append(specificity(image_array_opr, image_array_tpr))
finalMI.append(statistics.mean(log['metric'][-50:-1]))
fig, (ax1,ax2,ax3) = plt.subplots(1, 3, figsize=(15, 5))
ax1.scatter(finalMI,DSC)
ax1.set_title("DSC")
ax2.scatter(finalMI,SNS)
ax2.set_title("SNS")
ax3.scatter(finalMI,SPC)
ax3.set_title("SPC")
plt.show()
#%% Excercise 3b multiresolution registration.
#E.register(
# fixed_image=im1,
# moving_image=im2,
# parameters=['MR/parameters_samplespace2_MR.txt'],
# output_dir='results')
#%% 3D registration
E.register(fixed_image=im1,
moving_image=im2,
parameters=[r'MR/Parameters_Rigid.txt'],
output_dir='results')
#Parameters_Affine.txt
# Open the logfile into the dictionary L
L = elastix.logfile('results/IterationInfo.0.R0.txt')
plt.figure()
plt.plot(L['itnr'], L['metric'])
plt.show()
#%%
E.register(fixed_image=im1,
moving_image=im2,
parameters=[r'MR/Parameters_BSpline.txt'],
initial_transform=r'results/TransformParameters.0.txt',
output_dir='results')
#%%
L = elastix.logfile('results/IterationInfo.0.R0.txt')
# Plot the 'metric' against the iteration number 'itnr'
plt.figure()
plt.plot(L['itnr'], L['metric'])