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#!/usr/bin/env python
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
This script requires the nipy-data package to run. It is an example of
inter-subject affine registration using two MR-T1 images from the
'sulcal 2000' database acquired at CEA, SHFJ, Orsay, France. The
source is 'ammon' and the target is 'anubis'.
Usage:
python affine_matching [criterion][interpolation][optimizer]
Choices for criterion:
cc -- correlation coefficient
cr -- correlation ratio [DEFAULT]
crl1 -- correlation ratio, L1 norm version
mi -- mutual information
nmi -- normalized mutual information
je -- joint entropy
ce -- conditional entropy
Choices for interpolation method:
pv -- partial volume [DEFAULT]
tri -- trilinear
rand -- random
Choices for optimizer:
simplex
powell [DEFAULT]
steepest
cg
bfgs
Running this script will result in two files being created in the
working directory:
ammon_TO_anubis.nii
the source image resampled according to the target coordinate system
ammon_TO_anubis.npz
a numpy data file containing the 4x4 matrix that maps the source to
the target coordinate system
Author: Alexis Roche, 2009.
"""
from nipy.neurospin import register, transform
from nipy.utils import example_data
from nipy.io.imageformats import load as load_image, save as save_image
from os.path import join
import sys
import time
print('Scanning data directory...')
# Input images are provided with the nipy-data package
source = 'ammon'
target = 'anubis'
source_file = example_data.get_filename('neurospin','sulcal2000','nobias_'+source+'.nii.gz')
target_file = example_data.get_filename('neurospin','sulcal2000','nobias_'+target+'.nii.gz')
# Optional arguments
similarity = 'cr'
interp = 'pv'
optimizer = 'powell'
if len(sys.argv)>1:
similarity = sys.argv[1]
if len(sys.argv)>2:
interp = sys.argv[2]
if len(sys.argv)>3:
optimizer = sys.argv[3]
# Print messages
print ('Source brain: %s' % source)
print ('Target brain: %s' % target)
print ('Similarity measure: %s' % similarity)
print ('Optimizer: %s' % optimizer)
# Get data
print('Fetching image data...')
I = load_image(source_file)
J = load_image(target_file)
# Perform affine registration
# The output is an array-like object such that
# np.asarray(T) is a customary 4x4 matrix
print('Setting up registration...')
tic = time.time()
T = register(I, J,
similarity=similarity,
interp=interp,
optimizer=optimizer)
toc = time.time()
print(' Registration time: %f sec' % (toc-tic))
# Resample source image
print('Resampling source image...')
tic = time.time()
It = transform(I, T.inv(), reference=J)
toc = time.time()
print(' Resampling time: %f sec' % (toc-tic))
# Save resampled source
outfile = source+'_TO_'+target+'.nii'
print ('Saving resampled source in: %s' % outfile)
save_image(It, outfile)
# Save transformation matrix
import numpy as np
np.save(outfile, np.asarray(T))
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