def affine_registration_nipy(in_path, ref_path, out_path,
in_ref_mat = '', ref_in_mat = '',
T = None, extra_params={}):
"""
Affine registation and resampling. Use Histogram registration from nipy.
inputs:
in_path: path to the source (input) image.
ref_path: path to the target (reference) image.
out_path: path to use to save the registered image.
in_ref_mat: if bool(in_ref_mat) is True, save the 4x4 transformation
matrix to a text file <in_ref_mat>.
ref_in_mat: if bool(ref_in_mat) is True, save the reverse of the 4x4
transformation matrix to a text file <ref_in_mat>.
T: affine transformation to use. if None, T will be estimated using
HistogramRegistration and optimizers; if type(T) is not Affine,
T = Affine(array=T)
extra_params: extra parameters passing to HistogramRegistration,
HistogramRegistration.optimize, resample
return T
"""
source_image = load_image(in_path)
target_image = load_image(ref_path)
if T is None:
print('assess the affine transformation using histogram registration. ')
# R = HistogramRegistration(source_image, target_image)
R = AllFeatures(HistogramRegistration,extra_params).run(source_image, target_image)
# T = R.optimize('affine', optimizer='powell')
T = AllFeatures(R.optimize,extra_params).run('affine', optimizer='powell')
print('receive affine transformation %s' % T)
else:
if type(T) is not Affine:
print('create Affine from T')
T = Affine(array=T)
print('using a predefined affine:\n%s\nwith a 4x4 matrix:\n%s\n' % (T, T.as_affine()))
# It = resample(source_image, T.inv(), target_image)
It = AllFeatures(resample,extra_params).run(source_image, T.inv(), target_image)
# the second argument of resample takes an transformation from ref to mov
# so that's why we need T.inv() here
save_image(It, out_path)
if in_ref_mat:
np.savetxt(in_ref_mat, T.as_affine())
if ref_in_mat:
np.savetxt(ref_in_mat, T.inv().as_affine())
return T
from nipy import load_image
from nipy.algorithms.registration import (HistogramRegistration,
Affine,
resample)
# Load input images
fmri_img = load_image('mean_fmri.nii')
t1_img = load_image('T1.nii')
# First pass: rigid registration using mutual information
reg = HistogramRegistration(fmri_img, t1_img, similarity='mi')
T = reg.optimize('rigid')
# Second pass: 12-parameter affine registration using a custom variant
# of mutual information based on the Hellinger distance
def mymi(H):
# takes a 2D array representing the joint histogram as input
P = H / H.sum()
Pi = P.sum(0).reshape((H.shape[1], 1))
Pj = P.sum(1).reshape((H.shape[0], 1))
return np.sum((np.sqrt(P) - np.sqrt(Pi.T * Pj)) ** 2)
T2 = Affine(T.as_affine())
reg2 = HistogramRegistration(fmri_img, t1_img, similarity=mymi)
T2 = reg2.optimize(T2)
# Resample the fMRI image in T1 space
reg_fmri_img = resample(fmri_img, T2.inv(), reference=t1_img)
from nipy import load_image
from nipy.algorithms.registration import (HistogramRegistration, Affine,
resample)
# Load input images
fmri_img = load_image('mean_fmri.nii')
t1_img = load_image('T1.nii')
# First pass: rigid registration using mutual information
reg = HistogramRegistration(fmri_img, t1_img, similarity='mi')
T = reg.optimize('rigid')
# Second pass: 12-parameter affine registration using a custom variant
# of mutual information based on the Hellinger distance
def mymi(H):
# takes a 2D array representing the joint histogram as input
P = H / H.sum()
Pi = P.sum(0).reshape((H.shape[1], 1))
Pj = P.sum(1).reshape((H.shape[0], 1))
return np.sum((np.sqrt(P) - np.sqrt(Pi.T * Pj))**2)
T2 = Affine(T.as_affine())
reg2 = HistogramRegistration(fmri_img, t1_img, similarity=mymi)
T2 = reg2.optimize(T2)
# Resample the fMRI image in T1 space
reg_fmri_img = resample(fmri_img, T2.inv(), reference=t1_img)
'nobias_' + source + '.nii.gz')
target_file = example_data.get_filename('neurospin', 'sulcal2000',
'nobias_' + target + '.nii.gz')
# Optional arguments
similarity = 'cc'
interp = 'pv'
optimizer = 'powell'
# Make registration instance
I = load_image(source_file)
J = load_image(target_file)
R = HistogramRegistration(I, J, similarity=similarity, interp=interp)
# Global affine registration
A = Affine()
R.optimize(A)
#Jt = resample(J, A, reference=I)
Av = A.compose(Affine(I.affine))
Jat = resample(J, Av, reference=I, ref_voxel_coords=True)
save_image(Jat, 'affine_anubis_to_ammon.nii')
# Region matching
t0 = time.time()
##corners, size = get_blocks(I.shape, 3, 1, 0) #.5 size
##corners, size = get_blocks(I.shape, 6, 2, 0) #.75 size
##corners, size = get_blocks(I.shape, 6, 1, 0) # .5 size
corners, size = get_blocks(I.shape, 5, 2, 1)
