def rigidRegFunc(path,fileName,input_fixed,input_moving):
os.chdir(path)
reg = Registration()
# ants-registration parameters:
reg.inputs.fixed_image = input_fixed # fixed image
reg.inputs.moving_image = input_moving # moving image
reg.inputs.output_transform_prefix = path # file path
reg.inputs.transforms = ['Rigid'] # list of transformations
reg.inputs.transform_parameters = [(.5,)]
reg.inputs.number_of_iterations = [[40, 20, 10]]
# reg.inputs.number_of_iterations = [[1, 1, 1]]
reg.inputs.dimension = 3
reg.inputs.initial_moving_transform_com = True
#reg.inputs.invert_initial_moving_transform = True
reg.inputs.output_warped_image = True
reg.inputs.output_inverse_warped_image = True
reg.inputs.write_composite_transform = True
reg.inputs.collapse_output_transforms = True
reg.inputs.metric = ['MI'] # mutual information
reg.inputs.metric_weight = [1]
reg.inputs.radius_or_number_of_bins = [64]
reg.inputs.sampling_strategy = ['Regular']
reg.inputs.sampling_percentage = [0.5]
reg.inputs.terminal_output = 'allatonce'
reg.inputs.convergence_threshold = [1.e-6]
reg.inputs.convergence_window_size = [10]
reg.inputs.smoothing_sigmas = [[3, 1, 0]]
reg.inputs.sigma_units = ['vox']
reg.inputs.shrink_factors = [[ 2, 1, 0]]
reg.inputs.use_estimate_learning_rate_once = [True]
reg.inputs.use_histogram_matching = [True]
reg.terminal_output = 'none'
reg.inputs.num_threads = 4 # ?
reg.inputs.winsorize_lower_quantile = 0.025
reg.inputs.winsorize_upper_quantile = 0.95
reg.inputs.output_warped_image = True
#reg.inputs.collapse_linear_transforms_to_fixed_image_header = False
reg.inputs.output_warped_image = path + 'rigid_reg_'+ fileName
reg.cmdline
reg.run()
return
reg.inputs.write_composite_transform = True
reg.inputs.collapse_output_transforms = True
reg.inputs.metric = ['MI','Mattes'] # mutual information
reg.inputs.metric_weight = [1]*2
reg.inputs.radius_or_number_of_bins = [64]*2
reg.inputs.sampling_strategy = ['Regular']*2
reg.inputs.sampling_percentage = [0.5]*2
reg.inputs.terminal_output = 'allatonce'
reg.inputs.convergence_threshold = [1.e-6]*2
reg.inputs.convergence_window_size = [10]*2
reg.inputs.smoothing_sigmas = [[3, 1, 0]]*2
reg.inputs.sigma_units = ['vox']*2
reg.inputs.shrink_factors = [[ 2, 1, 0]]*2
reg.inputs.use_estimate_learning_rate_once = [True]*2
reg.inputs.use_histogram_matching = [True]*2
reg.terminal_output = 'none'
reg.inputs.num_threads = 4 # ?
reg.inputs.winsorize_lower_quantile = 0.025
reg.inputs.winsorize_upper_quantile = 0.95
reg.inputs.output_warped_image = True
#reg.inputs.collapse_linear_transforms_to_fixed_image_header = False
reg.inputs.output_warped_image = folder + 'registeredAtlas_' + fileName
reg.cmdline
reg.run()
at = ApplyTransforms()
# os.chdir(newPath)
fileNames = ['pbmap_GM.nii','pbmap_WM.nii','pbmap_CSF.nii','tissues.nii']
for j in range(4):
at.inputs.dimension = 3
at.inputs.input_image = atlasPath+fileNames[j]
