def compute_demons_step(self, forward_step=True):
r"""Demons step for SSD metric
Computes the demons step proposed by Vercauteren et al.[Vercauteren09]
for the SSD metric.
Parameters
----------
forward_step : boolean
if True, computes the Demons step in the forward direction
(warping the moving towards the static image). If False,
computes the backward step (warping the static image to the
moving image)
Returns
-------
displacement : array, shape (R, C, 2) or (S, R, C, 3)
the Demons step
References
----------
[Vercauteren09] Tom Vercauteren, Xavier Pennec, Aymeric Perchant,
Nicholas Ayache, "Diffeomorphic Demons: Efficient
Non-parametric Image Registration", Neuroimage 2009
"""
sigma_reg_2 = np.sum(self.static_spacing**2)/self.dim
if forward_step:
gradient = self.gradient_static
delta_field = self.static_image - self.moving_image
else:
gradient = self.gradient_moving
delta_field = self.moving_image - self.static_image
if self.dim == 2:
step, self.energy = ssd.compute_ssd_demons_step_2d(delta_field,
gradient,
sigma_reg_2,
None)
else:
step, self.energy = ssd.compute_ssd_demons_step_3d(delta_field,
gradient,
sigma_reg_2,
None)
for i in range(self.dim):
step[..., i] = ndimage.filters.gaussian_filter(step[..., i],
self.smooth)
return step
def compute_demons_step(self, forward_step=True):
r"""Demons step for SSD metric
Computes the demons step proposed by Vercauteren et al.[Vercauteren09]
for the SSD metric.
Parameters
----------
forward_step : boolean
if True, computes the Demons step in the forward direction
(warping the moving towards the static image). If False,
computes the backward step (warping the static image to the
moving image)
Returns
-------
displacement : array, shape (R, C, 2) or (S, R, C, 3)
the Demons step
References
----------
[Vercauteren09] Tom Vercauteren, Xavier Pennec, Aymeric Perchant,
Nicholas Ayache, "Diffeomorphic Demons: Efficient
Non-parametric Image Registration", Neuroimage 2009
"""
sigma_reg_2 = np.sum(self.static_spacing**2)/self.dim
if forward_step:
gradient = self.gradient_static
delta_field = self.static_image - self.moving_image
else:
gradient = self.gradient_moving
delta_field = self.moving_image - self.static_image
if self.dim == 2:
step, self.energy = ssd.compute_ssd_demons_step_2d(delta_field,
gradient,
sigma_reg_2,
None)
else:
step, self.energy = ssd.compute_ssd_demons_step_3d(delta_field,
gradient,
sigma_reg_2,
None)
for i in range(self.dim):
step[..., i] = ndimage.filters.gaussian_filter(step[..., i],
self.smooth)
return step
def test_compute_ssd_demons_step_3d():
r"""
Compares the output of the demons step in 3d against an analytical
step. The fixed image is given by $F(x) = \frac{1}{2}||x - c_f||^2$, the
moving image is given by $G(x) = \frac{1}{2}||x - c_g||^2$,
$x, c_f, c_g \in R^{3}$
References
----------
[Vercauteren09] Vercauteren, T., Pennec, X., Perchant, A., & Ayache, N.
(2009). Diffeomorphic demons: efficient non-parametric
image registration. NeuroImage, 45(1 Suppl), S61-72.
doi:10.1016/j.neuroimage.2008.10.040
"""
#Select arbitrary images' shape (same shape for both images)
sh = (20, 15, 10)
#Select arbitrary centers
c_f = np.asarray(sh)/2
c_g = c_f + 0.5
#Compute the identity vector field I(x) = x in R^2
x_0 = np.asarray(range(sh[0]))
x_1 = np.asarray(range(sh[1]))
x_2 = np.asarray(range(sh[2]))
X = np.ndarray(sh + (3,), dtype = np.float64)
O = np.ones(sh)
X[...,0]= x_0[:, None, None] * O
X[...,1]= x_1[None, :, None] * O
X[...,2]= x_2[None, None, :] * O
#Compute the gradient fields of F and G
np.random.seed(1137271)
grad_F = X - c_f
grad_G = X - c_g
Fnoise = np.random.ranf(np.size(grad_F)).reshape(grad_F.shape) * grad_F.max() * 0.1
Fnoise = Fnoise.astype(floating)
grad_F += Fnoise
Gnoise = np.random.ranf(np.size(grad_G)).reshape(grad_G.shape) * grad_G.max() * 0.1
Gnoise = Gnoise.astype(floating)
grad_G += Gnoise
#The squared norm of grad_G to be used later
sq_norm_grad_G = np.sum(grad_G**2,-1)
#Compute F and G
F = 0.5*np.sum(grad_F**2,-1)
G = 0.5*sq_norm_grad_G
Fnoise = np.random.ranf(np.size(F)).reshape(F.shape) * F.max() * 0.1
Fnoise = Fnoise.astype(floating)
F += Fnoise
Gnoise = np.random.ranf(np.size(G)).reshape(G.shape) * G.max() * 0.1
Gnoise = Gnoise.astype(floating)
G += Gnoise
delta_field = np.array(G - F, dtype = floating)
#Select some pixels to force gradient = 0 and F=G
random_labels = np.random.randint(0, 2, sh[0]*sh[1]*sh[2])
random_labels = random_labels.reshape(sh)
F[random_labels == 0] = G[random_labels == 0]
delta_field[random_labels == 0] = 0
grad_G[random_labels == 0, ...] = 0
sq_norm_grad_G[random_labels == 0, ...] = 0
#Set arbitrary values for $\sigma_i$ (eq. 4 in [Vercauteren09])
#The original Demons algorithm used simply |F(x) - G(x)| as an
#estimator, so let's use it as well
sigma_i_sq = (F - G)**2
#Now select arbitrary parameters for $\sigma_x$ (eq 4 in [Vercauteren09])
for sigma_x_sq in [0.01, 1.5, 4.2]:
#Directly compute the demons step according to eq. 4 in [Vercauteren09]
num = (sigma_x_sq * (F - G))[random_labels == 1]
den = (sigma_x_sq * sq_norm_grad_G + sigma_i_sq)[random_labels == 1]
expected = (-1 * np.array(grad_G)) #This is $J^{P}$ in eq. 4 [Vercauteren09]
expected[random_labels == 1, 0] *= num / den
expected[random_labels == 1, 1] *= num / den
expected[random_labels == 1, 2] *= num / den
expected[random_labels == 0, ...] = 0
#Now compute it using the implementation under test
actual = np.empty_like(expected, dtype=floating)
ssd.compute_ssd_demons_step_3d(delta_field,
np.array(grad_G, dtype = floating),
sigma_x_sq,
actual)
assert_array_almost_equal(actual, expected)