def dipy_align(static, static_grid2world, moving, moving_grid2world, prealign=None):
r""" Full rigid registration with Dipy's imaffine module
Here we implement an extra optimization heuristic: move the geometric
centers of the images to the origin. Imaffine does not do this by default
because we want to give the user as much control of the optimization
process as possible.
"""
# Bring the center of the moving image to the origin
c_moving = tuple(0.5 * np.array(moving.shape, dtype=np.float64))
c_moving = moving_grid2world.dot(c_moving + (1,))
correction_moving = np.eye(4, dtype=np.float64)
correction_moving[:3, 3] = -1 * c_moving[:3]
centered_moving_aff = correction_moving.dot(moving_grid2world)
# Bring the center of the static image to the origin
c_static = tuple(0.5 * np.array(static.shape, dtype=np.float64))
c_static = static_grid2world.dot(c_static + (1,))
correction_static = np.eye(4, dtype=np.float64)
correction_static[:3, 3] = -1 * c_static[:3]
centered_static_aff = correction_static.dot(static_grid2world)
dim = len(static.shape)
metric = MutualInformationMetric(nbins=32, sampling_proportion=0.3)
level_iters = [10000, 1000, 100]
affr = AffineRegistration(metric=metric, level_iters=level_iters)
affr.verbosity = VerbosityLevels.DEBUG
# metric.verbosity = VerbosityLevels.DEBUG
# Registration schedule: center-of-mass then translation, then rigid and then affine
if prealign is None:
prealign = "mass"
transforms = ["TRANSLATION", "RIGID", "AFFINE"]
sol = np.eye(dim + 1)
for transform_name in transforms:
transform = regtransforms[(transform_name, dim)]
print("Optimizing: %s" % (transform_name,))
x0 = None
sol = affr.optimize(
static, moving, transform, x0, centered_static_aff, centered_moving_aff, starting_affine=prealign
)
prealign = sol.affine.copy()
# Now bring the geometric centers back to their original location
fixed = np.linalg.inv(correction_moving).dot(sol.affine.dot(correction_static))
sol.set_affine(fixed)
sol.domain_grid2world = static_grid2world
sol.codomain_grid2world = moving_grid2world
return sol
def dipy_align(static,
static_grid2world,
moving,
moving_grid2world,
prealign=None):
r''' Full rigid registration with Dipy's imaffine module
Here we implement an extra optimization heuristic: move the geometric
centers of the images to the origin. Imaffine does not do this by default
because we want to give the user as much control of the optimization
process as possible.
'''
# Bring the center of the moving image to the origin
c_moving = tuple(0.5 * np.array(moving.shape, dtype=np.float64))
c_moving = moving_grid2world.dot(c_moving + (1, ))
correction_moving = np.eye(4, dtype=np.float64)
correction_moving[:3, 3] = -1 * c_moving[:3]
centered_moving_aff = correction_moving.dot(moving_grid2world)
# Bring the center of the static image to the origin
c_static = tuple(0.5 * np.array(static.shape, dtype=np.float64))
c_static = static_grid2world.dot(c_static + (1, ))
correction_static = np.eye(4, dtype=np.float64)
correction_static[:3, 3] = -1 * c_static[:3]
centered_static_aff = correction_static.dot(static_grid2world)
dim = len(static.shape)
metric = MutualInformationMetric(nbins=32, sampling_proportion=0.3)
level_iters = [10000, 1000, 100]
affr = AffineRegistration(metric=metric, level_iters=level_iters)
affr.verbosity = VerbosityLevels.DEBUG
#metric.verbosity = VerbosityLevels.DEBUG
# Registration schedule: center-of-mass then translation, then rigid and then affine
if prealign is None:
prealign = 'mass'
transforms = ['TRANSLATION', 'RIGID', 'AFFINE']
sol = np.eye(dim + 1)
for transform_name in transforms:
transform = regtransforms[(transform_name, dim)]
print('Optimizing: %s' % (transform_name, ))
x0 = None
sol = affr.optimize(static,
moving,
transform,
x0,
centered_static_aff,
centered_moving_aff,
starting_affine=prealign)
prealign = sol.affine.copy()
# Now bring the geometric centers back to their original location
fixed = np.linalg.inv(correction_moving).dot(
sol.affine.dot(correction_static))
sol.set_affine(fixed)
sol.domain_grid2world = static_grid2world
sol.codomain_grid2world = moving_grid2world
return sol
