def get_synthetic_warped_circle(nslices):
#get a subsampled circle
fname_cicle = get_data('reg_o')
circle = np.load(fname_cicle)[::4,::4].astype(floating)
#create a synthetic invertible map and warp the circle
d, dinv = vfu.create_harmonic_fields_2d(64, 64, 0.1, 4)
d = np.asarray(d, dtype=floating)
dinv = np.asarray(dinv, dtype=floating)
mapping = DiffeomorphicMap(2, (64, 64))
mapping.forward, mapping.backward = d, dinv
wcircle = mapping.transform(circle)
if(nslices == 1):
return circle, wcircle
#normalize and form the 3d by piling slices
circle = (circle-circle.min())/(circle.max() - circle.min())
circle_3d = np.ndarray(circle.shape + (nslices,), dtype=floating)
circle_3d[...] = circle[...,None]
circle_3d[...,0] = 0
circle_3d[...,-1] = 0
#do the same with the warped circle
wcircle = (wcircle-wcircle.min())/(wcircle.max() - wcircle.min())
wcircle_3d = np.ndarray(wcircle.shape + (nslices,), dtype=floating)
wcircle_3d[...] = wcircle[...,None]
wcircle_3d[...,0] = 0
wcircle_3d[...,-1] = 0
return circle_3d, wcircle_3d
def get_warped_stacked_image(image, nslices, b, m):
r""" Creates a volume by stacking copies of a deformed image
The image is deformed under an invertible field, and a 3D volume is
generated as follows:
the first and last `nslices`//3 slices are filled with zeros
to simulate background. The remaining middle slices are filled with
copies of the deformed `image` under the action of the invertible
field.
Parameters
----------
image : 2d array shape(r, c)
the image to be deformed
nslices : int
the number of slices in the final volume
b, m : float
parameters of the harmonic field (as in [1]).
Returns
-------
vol : array shape(r, c) if `nslices`==1 else (r, c, `nslices`)
the volumed generated using the undeformed image
wvol : array shape(r, c) if `nslices`==1 else (r, c, `nslices`)
the volumed generated using the warped image
References
----------
[1] Chen, M., Lu, W., Chen, Q., Ruchala, K. J., & Olivera, G. H. (2008).
A simple fixed-point approach to invert a deformation field.
Medical Physics, 35(1), 81. doi:10.1118/1.2816107
"""
shape = image.shape
# create a synthetic invertible map and warp the circle
d, dinv = vfu.create_harmonic_fields_2d(shape[0], shape[1], b, m)
d = np.asarray(d, dtype=floating)
dinv = np.asarray(dinv, dtype=floating)
mapping = DiffeomorphicMap(2, shape)
mapping.forward, mapping.backward = d, dinv
wimage = mapping.transform(image)
if (nslices == 1):
return image, wimage
# normalize and form the 3d by piling slices
image = image.astype(floating)
image = (image - image.min()) / (image.max() - image.min())
zero_slices = nslices // 3
vol = np.zeros(shape=image.shape + (nslices, ))
vol[..., zero_slices:(2 * zero_slices)] = image[..., None]
wvol = np.zeros(shape=image.shape + (nslices, ))
wvol[..., zero_slices:(2 * zero_slices)] = wimage[..., None]
return vol, wvol
def get_warped_stacked_image(image, nslices, b, m):
r""" Creates a volume by stacking copies of a deformed image
The image is deformed under an invertible field, and a 3D volume is
generated as follows:
the first and last `nslices`//3 slices are filled with zeros
to simulate background. The remaining middle slices are filled with
copies of the deformed `image` under the action of the invertible
field.
Parameters
----------
image : 2d array shape(r, c)
the image to be deformed
nslices : int
the number of slices in the final volume
b, m : float
parameters of the harmonic field (as in [1]).
Returns
-------
vol : array shape(r, c) if `nslices`==1 else (r, c, `nslices`)
the volumed generated using the undeformed image
wvol : array shape(r, c) if `nslices`==1 else (r, c, `nslices`)
the volumed generated using the warped image
References
----------
[1] Chen, M., Lu, W., Chen, Q., Ruchala, K. J., & Olivera, G. H. (2008).
A simple fixed-point approach to invert a deformation field.
Medical Physics, 35(1), 81. doi:10.1118/1.2816107
"""
shape = image.shape
# create a synthetic invertible map and warp the circle
d, dinv = vfu.create_harmonic_fields_2d(shape[0], shape[1], b, m)
d = np.asarray(d, dtype=floating)
dinv = np.asarray(dinv, dtype=floating)
mapping = DiffeomorphicMap(2, shape)
mapping.forward, mapping.backward = d, dinv
wimage = mapping.transform(image)
if(nslices == 1):
return image, wimage
# normalize and form the 3d by piling slices
image = image.astype(floating)
image = (image - image.min()) / (image.max() - image.min())
zero_slices = nslices // 3
vol = np.zeros(shape=image.shape + (nslices,))
vol[..., zero_slices:(2 * zero_slices)] = image[..., None]
wvol = np.zeros(shape=image.shape + (nslices,))
wvol[..., zero_slices:(2 * zero_slices)] = wimage[..., None]
return vol, wvol
