def test_exec():
target='target/IBSR_01_ana_strip.nii.gz'
reference='reference/t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz'
affine='IBSR_01_ana_strip_t1_icbm_normal_1mm_pn0_rf0_peeledAffine.txt'
paramiter='30x30x0'
inversion_iter='20'
inversion_tolerance='1e-3'
report_status=True
print('Registering %s to %s'%(target, reference))
sys.stdout.flush()
####Initialize parameter dictionaries####
metric_parameters = {
'max_step_length':0.25,
'sigma_diff':3.0,
'radius':4}
similarity_metric = CCMetric(3, metric_parameters)
optimizer_parameters = {
'max_iter':[int(i) for i in paramiter.split(',')],
'inversion_iter':int(inversion_iter),
'inversion_tolerance':float(inversion_tolerance),
'report_status':True if report_status else False}
moving = nib.load(target)
moving_affine = moving.get_affine()
fixed = nib.load(reference)
fixed_affine = fixed.get_affine()
print 'Affine:', affine
if not affine:
transform = np.eye(4)
else:
transform = rcommon.readAntsAffine(affine)
init_affine = np.linalg.inv(moving_affine).dot(transform.dot(fixed_affine))
#print initAffine
moving = moving.get_data().squeeze().astype(np.float64)
fixed = fixed.get_data().squeeze().astype(np.float64)
moving = moving.copy(order='C')
fixed = fixed.copy(order='C')
moving = (moving-moving.min())/(moving.max()-moving.min())
fixed = (fixed-fixed.min())/(fixed.max()-fixed.min())
###################Run registration##################
update_rule = UpdateRule.Composition()
registration_optimizer = SymmetricRegistrationOptimizer(
fixed, moving, None, init_affine, similarity_metric, update_rule,
optimizer_parameters)
registration_optimizer.optimize()
#displacement = registration_optimizer.get_forward()
#inverse = registration_optimizer.get_backward()
del registration_optimizer
del similarity_metric
del update_rule
def register_3d(params):
r'''
Runs the non-linear registration with the parsed parameters
'''
print('Registering %s to %s'%(params.target, params.reference))
sys.stdout.flush()
####Initialize parameter dictionaries####
metric_name=params.metric[0:params.metric.find('[')]
metric_params_list=params.metric[params.metric.find('[')+1:params.metric.find(']')].split(',')
if metric_name=='EM':
metric_parameters = {
'max_step_length':float(metric_params_list[0]),
'lambda':float(metric_params_list[1]),
'q_levels':int(metric_params_list[2]),
'max_inner_iter':int(metric_params_list[3]),
'use_double_gradient':False if params.single_gradient else True}
similarity_metric = EMMetric(3, metric_parameters)
elif metric_name=='CC':
metric_parameters = {
'max_step_length':float(metric_params_list[0]),
'sigma_diff':float(metric_params_list[1]),
'radius':int(metric_params_list[2])}
similarity_metric = CCMetric(3, metric_parameters)
optimizer_parameters = {
'max_iter':[int(i) for i in params.iter.split(',')],
'inversion_iter':int(params.inversion_iter),
'inversion_tolerance':float(params.inversion_tolerance),
'report_status':True if params.report_status else False}
moving = nib.load(params.target)
moving_affine = moving.get_affine()
fixed = nib.load(params.reference)
fixed_affine = fixed.get_affine()
print 'Affine:', params.affine
if not params.affine:
transform = np.eye(4)
else:
transform = rcommon.readAntsAffine(params.affine)
init_affine = np.linalg.inv(moving_affine).dot(transform.dot(fixed_affine))
#print initAffine
moving = moving.get_data().squeeze().astype(np.float64)
fixed = fixed.get_data().squeeze().astype(np.float64)
moving = moving.copy(order='C')
fixed = fixed.copy(order='C')
moving = (moving-moving.min())/(moving.max()-moving.min())
fixed = (fixed-fixed.min())/(fixed.max()-fixed.min())
###################Run registration##################
update_rule = UpdateRule.Composition()
registration_optimizer = SymmetricRegistrationOptimizer(
fixed, moving, None, init_affine, similarity_metric, update_rule,
optimizer_parameters)
registration_optimizer.optimize()
displacement = registration_optimizer.get_forward()
inverse = registration_optimizer.get_backward()
del registration_optimizer
del similarity_metric
del update_rule
save_registration_results(init_affine, displacement, inverse, params)
