def test_icm_square():
com = ConstantObservationModel()
icm = IteratedConditionalModes()
initial_segmentation = square.copy()
mu, sigma = com.seg_stats(square_1, initial_segmentation,
nclasses)
sigmasq = sigma ** 2
npt.assert_(mu[0] >= 0.0)
npt.assert_(mu[1] >= 0.0)
npt.assert_(mu[2] >= 0.0)
npt.assert_(mu[3] >= 0.0)
npt.assert_(sigmasq[0] >= 0.0)
npt.assert_(sigmasq[1] >= 0.0)
npt.assert_(sigmasq[2] >= 0.0)
npt.assert_(sigmasq[3] >= 0.0)
negll = com.negloglikelihood(square_1, mu, sigmasq, nclasses)
final_segmentation_1 = np.empty_like(square_1)
final_segmentation_2 = np.empty_like(square_1)
beta = 0.0
for i in range(max_iter):
print('\n')
print('>> Iteration: ' + str(i))
print('\n')
final_segmentation_1, energy_1 = icm.icm_ising(negll, beta,
initial_segmentation)
initial_segmentation = final_segmentation_1.copy()
beta = 2
initial_segmentation = square.copy()
for j in range(max_iter):
print('\n')
print('>> Iteration: ' + str(j))
print('\n')
final_segmentation_2, energy_2 = icm.icm_ising(negll, beta,
initial_segmentation)
initial_segmentation = final_segmentation_2.copy()
difference_map = np.abs(final_segmentation_1 - final_segmentation_2)
npt.assert_(np.abs(np.sum(difference_map)) != 0)
def test_greyscale_iter():
max_iter = 15
beta = np.float64(0.1)
com = ConstantObservationModel()
icm = IteratedConditionalModes()
mu, sigma = com.initialize_param_uniform(image, nclasses)
sigmasq = sigma ** 2
neglogl = com.negloglikelihood(image, mu, sigmasq, nclasses)
initial_segmentation = icm.initialize_maximum_likelihood(neglogl)
npt.assert_(initial_segmentation.max() == nclasses - 1)
npt.assert_(initial_segmentation.min() == 0)
mu, sigma = com.seg_stats(image, initial_segmentation, nclasses)
sigmasq = sigma ** 2
npt.assert_(mu[0] >= 0.0)
npt.assert_(mu[1] >= 0.0)
npt.assert_(mu[2] >= 0.0)
npt.assert_(mu[3] >= 0.0)
npt.assert_(sigmasq[0] >= 0.0)
npt.assert_(sigmasq[1] >= 0.0)
npt.assert_(sigmasq[2] >= 0.0)
npt.assert_(sigmasq[3] >= 0.0)
if background_noise:
zero = np.zeros_like(image) + 0.001
zero_noise = add_noise(zero, 10000, 1, noise_type='gaussian')
image_gauss = np.where(image == 0, zero_noise, image)
else:
image_gauss = image
final_segmentation = np.empty_like(image)
seg_init = initial_segmentation.copy()
energies = []
for i in range(max_iter):
PLN = icm.prob_neighborhood(initial_segmentation, beta,
nclasses)
npt.assert_(np.all((PLN >= 0) & (PLN <= 1.0)))
if beta == 0.0:
npt.assert_almost_equal(PLN[50, 50, 1, 0], 0.25, True)
npt.assert_almost_equal(PLN[50, 50, 1, 1], 0.25, True)
npt.assert_almost_equal(PLN[50, 50, 1, 2], 0.25, True)
npt.assert_almost_equal(PLN[50, 50, 1, 3], 0.25, True)
npt.assert_almost_equal(PLN[147, 129, 1, 0], 0.25, True)
npt.assert_almost_equal(PLN[147, 129, 1, 1], 0.25, True)
npt.assert_almost_equal(PLN[147, 129, 1, 2], 0.25, True)
npt.assert_almost_equal(PLN[147, 129, 1, 3], 0.25, True)
npt.assert_almost_equal(PLN[61, 152, 1, 0], 0.25, True)
npt.assert_almost_equal(PLN[61, 152, 1, 1], 0.25, True)
npt.assert_almost_equal(PLN[61, 152, 1, 2], 0.25, True)
npt.assert_almost_equal(PLN[61, 152, 1, 3], 0.25, True)
npt.assert_almost_equal(PLN[100, 100, 1, 0], 0.25, True)
npt.assert_almost_equal(PLN[100, 100, 1, 1], 0.25, True)
npt.assert_almost_equal(PLN[100, 100, 1, 2], 0.25, True)
npt.assert_almost_equal(PLN[100, 100, 1, 3], 0.25, True)
PLY = com.prob_image(image_gauss, nclasses, mu, sigmasq, PLN)
npt.assert_(np.all((PLY >= 0) & (PLY <= 1.0)))
npt.assert_(PLY[50, 50, 1, 0] > PLY[50, 50, 1, 1])
npt.assert_(PLY[50, 50, 1, 0] > PLY[50, 50, 1, 2])
npt.assert_(PLY[50, 50, 1, 0] > PLY[50, 50, 1, 3])
npt.assert_(PLY[100, 100, 1, 3] > PLY[100, 100, 1, 0])
npt.assert_(PLY[100, 100, 1, 3] > PLY[100, 100, 1, 1])
npt.assert_(PLY[100, 100, 1, 3] > PLY[100, 100, 1, 2])
mu_upd, sigmasq_upd = com.update_param(image_gauss, PLY, mu, nclasses)
npt.assert_(mu_upd[0] >= 0.0)
npt.assert_(mu_upd[1] >= 0.0)
npt.assert_(mu_upd[2] >= 0.0)
npt.assert_(mu_upd[3] >= 0.0)
npt.assert_(sigmasq_upd[0] >= 0.0)
npt.assert_(sigmasq_upd[1] >= 0.0)
npt.assert_(sigmasq_upd[2] >= 0.0)
npt.assert_(sigmasq_upd[3] >= 0.0)
negll = com.negloglikelihood(image_gauss,
mu_upd, sigmasq_upd, nclasses)
npt.assert_(negll[50, 50, 1, 0] < negll[50, 50, 1, 1])
npt.assert_(negll[50, 50, 1, 0] < negll[50, 50, 1, 2])
npt.assert_(negll[50, 50, 1, 0] < negll[50, 50, 1, 3])
npt.assert_(negll[100, 100, 1, 3] < negll[100, 100, 1, 0])
npt.assert_(negll[100, 100, 1, 3] < negll[100, 100, 1, 1])
npt.assert_(negll[100, 100, 1, 3] < negll[100, 100, 1, 2])
final_segmentation, energy = icm.icm_ising(negll, beta,
initial_segmentation)
print(energy[energy > -np.inf].sum())
energies.append(energy[energy > -np.inf].sum())
initial_segmentation = final_segmentation.copy()
mu = mu_upd.copy()
sigmasq = sigmasq_upd.copy()
npt.assert_(energies[-1] < energies[0])
difference_map = np.abs(seg_init - final_segmentation)
npt.assert_(np.abs(np.sum(difference_map)) != 0)
def test_square_iter():
com = ConstantObservationModel()
icm = IteratedConditionalModes()
initial_segmentation = square
mu, sigma = com.seg_stats(square_gauss, initial_segmentation,
nclasses)
sigmasq = sigma ** 2
npt.assert_(mu[0] >= 0.0)
npt.assert_(mu[1] >= 0.0)
npt.assert_(mu[2] >= 0.0)
npt.assert_(mu[3] >= 0.0)
npt.assert_(sigmasq[0] >= 0.0)
npt.assert_(sigmasq[1] >= 0.0)
npt.assert_(sigmasq[2] >= 0.0)
npt.assert_(sigmasq[3] >= 0.0)
final_segmentation = np.empty_like(square_gauss)
seg_init = initial_segmentation.copy()
energies = []
for i in range(max_iter):
print('\n')
print('>> Iteration: ' + str(i))
print('\n')
PLN = icm.prob_neighborhood(initial_segmentation, beta,
nclasses)
npt.assert_(np.all((PLN >= 0) & (PLN <= 1.0)))
if beta == 0.0:
npt.assert_(PLN[25, 25, 1, 0] == 0.25)
npt.assert_(PLN[25, 25, 1, 1] == 0.25)
npt.assert_(PLN[25, 25, 1, 2] == 0.25)
npt.assert_(PLN[25, 25, 1, 3] == 0.25)
npt.assert_(PLN[50, 50, 1, 0] == 0.25)
npt.assert_(PLN[50, 50, 1, 1] == 0.25)
npt.assert_(PLN[50, 50, 1, 2] == 0.25)
npt.assert_(PLN[50, 50, 1, 3] == 0.25)
npt.assert_(PLN[90, 90, 1, 0] == 0.25)
npt.assert_(PLN[90, 90, 1, 1] == 0.25)
npt.assert_(PLN[90, 90, 1, 2] == 0.25)
npt.assert_(PLN[90, 90, 1, 3] == 0.25)
npt.assert_(PLN[125, 125, 1, 0] == 0.25)
npt.assert_(PLN[125, 125, 1, 1] == 0.25)
npt.assert_(PLN[125, 125, 1, 2] == 0.25)
npt.assert_(PLN[125, 125, 1, 3] == 0.25)
PLY = com.prob_image(square_gauss, nclasses, mu, sigmasq, PLN)
npt.assert_(np.all((PLY >= 0) & (PLY <= 1.0)))
npt.assert_(PLY[25, 25, 1, 0] > PLY[25, 25, 1, 1])
npt.assert_(PLY[25, 25, 1, 0] > PLY[25, 25, 1, 2])
npt.assert_(PLY[25, 25, 1, 0] > PLY[25, 25, 1, 3])
npt.assert_(PLY[125, 125, 1, 3] > PLY[125, 125, 1, 0])
npt.assert_(PLY[125, 125, 1, 3] > PLY[125, 125, 1, 1])
npt.assert_(PLY[125, 125, 1, 3] > PLY[125, 125, 1, 2])
mu_upd, sigmasq_upd = com.update_param(square_gauss, PLY, mu, nclasses)
npt.assert_(mu_upd[0] >= 0.0)
npt.assert_(mu_upd[1] >= 0.0)
npt.assert_(mu_upd[2] >= 0.0)
npt.assert_(mu_upd[3] >= 0.0)
npt.assert_(sigmasq_upd[0] >= 0.0)
npt.assert_(sigmasq_upd[1] >= 0.0)
npt.assert_(sigmasq_upd[2] >= 0.0)
npt.assert_(sigmasq_upd[3] >= 0.0)
negll = com.negloglikelihood(square_gauss,
mu_upd, sigmasq_upd, nclasses)
npt.assert_(negll[25, 25, 1, 0] < negll[25, 25, 1, 1])
npt.assert_(negll[25, 25, 1, 0] < negll[25, 25, 1, 2])
npt.assert_(negll[25, 25, 1, 0] < negll[25, 25, 1, 3])
npt.assert_(negll[100, 100, 1, 3] < negll[125, 125, 1, 0])
npt.assert_(negll[100, 100, 1, 3] < negll[125, 125, 1, 1])
npt.assert_(negll[100, 100, 1, 3] < negll[125, 125, 1, 2])
final_segmentation, energy = icm.icm_ising(negll, beta,
initial_segmentation)
energies.append(energy[energy > -np.inf].sum())
initial_segmentation = final_segmentation.copy()
mu = mu_upd.copy()
sigmasq = sigmasq_upd.copy()
difference_map = np.abs(seg_init - final_segmentation)
npt.assert_(np.abs(np.sum(difference_map)) == 0.0)
def classify(self, image, nclasses, beta, tolerance=None, max_iter=None):
r"""
This method uses the Maximum a posteriori - Markov Random Field
approach for segmentation by using the Iterative Conditional Modes and
Expectation Maximization to estimate the parameters.
Parameters
----------
image : ndarray,
3D structural image.
nclasses : int,
number of desired classes.
beta : float,
smoothing parameter, the higher this number the smoother the
output will be.
tolerance: float,
value that defines the percentage of change tolerated to
prevent the ICM loop to stop. Default is 1e-05.
max_iter : float,
fixed number of desired iterations. Default is 100.
If the user only specifies this parameter, the tolerance
value will not be considered. If none of these two
parameters
Returns
-------
initial_segmentation : ndarray,
3D segmented image with all tissue types
specified in nclasses.
final_segmentation : ndarray,
3D final refined segmentation containing all
tissue types.
PVE : ndarray,
3D probability map of each tissue type.
"""
nclasses = nclasses + 1 # One extra class for the background
energy_sum = [1e-05]
com = ConstantObservationModel()
icm = IteratedConditionalModes()
if image.max() > 1:
image = np.interp(image, [0, image.max()], [0.0, 1.0])
mu, sigma = com.initialize_param_uniform(image, nclasses)
p = np.argsort(mu)
mu = mu[p]
sigma = sigma[p]
sigmasq = sigma**2
neglogl = com.negloglikelihood(image, mu, sigmasq, nclasses)
seg_init = icm.initialize_maximum_likelihood(neglogl)
mu, sigma = com.seg_stats(image, seg_init, nclasses)
sigmasq = sigma**2
zero = np.zeros_like(image) + 0.001
zero_noise = add_noise(zero, 10000, 1, noise_type='gaussian')
image_gauss = np.where(image == 0, zero_noise, image)
final_segmentation = np.empty_like(image)
initial_segmentation = seg_init.copy()
if max_iter is not None and tolerance is None:
for i in range(max_iter):
if self.verbose:
print('>> Iteration: ' + str(i))
PLN = icm.prob_neighborhood(seg_init, beta, nclasses)
PVE = com.prob_image(image_gauss, nclasses, mu, sigmasq, PLN)
mu_upd, sigmasq_upd = com.update_param(image_gauss, PVE, mu,
nclasses)
ind = np.argsort(mu_upd)
mu_upd = mu_upd[ind]
sigmasq_upd = sigmasq_upd[ind]
negll = com.negloglikelihood(image_gauss, mu_upd, sigmasq_upd,
nclasses)
final_segmentation, energy = icm.icm_ising(
negll, beta, seg_init)
if self.save_history:
self.segmentations.append(final_segmentation)
self.pves.append(PVE)
self.energies.append(energy)
self.energies_sum.append(energy[energy > -np.inf].sum())
seg_init = final_segmentation.copy()
mu = mu_upd.copy()
sigmasq = sigmasq_upd.copy()
else:
max_iter = 100
if tolerance is None:
tolerance = 1e-05
for i in range(max_iter):
if self.verbose:
print('>> Iteration: ' + str(i))
PLN = icm.prob_neighborhood(seg_init, beta, nclasses)
PVE = com.prob_image(image_gauss, nclasses, mu, sigmasq, PLN)
mu_upd, sigmasq_upd = com.update_param(image_gauss, PVE, mu,
nclasses)
ind = np.argsort(mu_upd)
mu_upd = mu_upd[ind]
sigmasq_upd = sigmasq_upd[ind]
negll = com.negloglikelihood(image_gauss, mu_upd, sigmasq_upd,
nclasses)
final_segmentation, energy = icm.icm_ising(
negll, beta, seg_init)
energy_sum.append(energy[energy > -np.inf].sum())
if self.save_history:
self.segmentations.append(final_segmentation)
self.pves.append(PVE)
self.energies.append(energy)
self.energies_sum.append(energy[energy > -np.inf].sum())
if i % 10 == 0 and i != 0:
tol = tolerance * (np.amax(energy_sum) -
np.amin(energy_sum))
test_dist = np.absolute(
np.amax(energy_sum[np.size(energy_sum) - 5:i]) -
np.amin(energy_sum[np.size(energy_sum) - 5:i]))
if test_dist < tol:
break
seg_init = final_segmentation.copy()
mu = mu_upd.copy()
sigmasq = sigmasq_upd.copy()
PVE = PVE[..., 1:]
return initial_segmentation, final_segmentation, PVE