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)
