def test_weighted_features_elaborate():
#Slightly more elaborate, similar to likelihood computations needed for
# lung segmentation
left_polynomial_feature_map = PolynomialFeatureMap( [I1,I2],[0,1,2] )
left_polynomial_feature_map.compute_num_terms()
left_weights_temp = [0.002149, -0.002069, 5.258745]
left_weights = [left_weights_temp[2]*left_weights_temp[2], 2*left_weights_temp[0]*left_weights_temp[2], \
2*left_weights_temp[1]*left_weights_temp[2], left_weights_temp[0]*left_weights_temp[0], \
2*left_weights_temp[0]*left_weights_temp[1], left_weights_temp[1]*left_weights_temp[1] ]
left_lambda = 1.0
left_weighted_density = ExpWeightedFeatureMapDensity([I1,I2], left_weights, left_polynomial_feature_map, left_lambda)
left_likelihood = left_weighted_density.compute()
#should be equivelent to: exp(-(alpha_est_non(1)*Ival + alpha_est_non(2)*Dval+alpha_est_non(3)))^2
assert left_likelihood.all() == np.exp(-np.power( (left_weights_temp[0]*I1 + left_weights_temp[1]*I2+ left_weights_temp[2]) , 2) ).all()
def test_weighted_features():
sys.stderr.write(str(I1)+"\n")
sys.stderr.write(str(I2)+"\n")
my_polynomial_feature_map = PolynomialFeatureMap( [I1,I2],[2] )
my_polynomial_feature_map.compute_num_terms()
assert my_polynomial_feature_map.num_terms == 3
#define the weights
#exp(-(alpha_est_non(1)*Ival + alpha_est_non(2)*Dval+alpha_est_non(3)))^2
the_weights = [1.0, 0.0, 0.0 ]
the_lambda = 1.0
#(self, im_feature_vecs, weights, feature_map, lamda)
my_weighted_density = ExpWeightedFeatureMapDensity([I1,I2], the_weights, my_polynomial_feature_map, the_lambda)
my_likelihood = my_weighted_density.compute()
assert my_likelihood.all() == np.exp(-np.power(I1,2)).all()
the_weights = [0.0, 1.0, 0.0 ]
my_weighted_density = ExpWeightedFeatureMapDensity([I1,I2], the_weights, my_polynomial_feature_map, the_lambda)
my_likelihood = my_weighted_density.compute()
assert my_likelihood.all() == np.exp(-np.power(I2,2)).all()
the_weights = [0.3, 0.5, 1.3 ]
my_weighted_density = ExpWeightedFeatureMapDensity([I1,I2], the_weights, my_polynomial_feature_map, 1.0)
my_likelihood = my_weighted_density.compute()
assert my_likelihood.all() == np.exp(-(the_weights[0]*np.power(I1,2) + the_weights[1]*I1*I2+ the_weights[2]*np.power(I2,2)) ).all()
def test_polynomial_feature_map():
sys.stderr.write(str(I1) + "\n")
sys.stderr.write(str(I2) + "\n")
my_polynomial_feature_map = PolynomialFeatureMap([I1, I2], [1, 2])
my_polynomial_feature_map.compute_num_terms()
first_element = my_polynomial_feature_map.get_mapped_feature_vec_element(2)
result = np.multiply(I1, I1)
assert (first_element == result).all()
first_element = my_polynomial_feature_map.get_mapped_feature_vec_element(0)
result = I1
assert (first_element == result).all()
my_polynomial_feature_map = PolynomialFeatureMap([I1, I2], [0])
my_polynomial_feature_map.compute_num_terms()
first_element = my_polynomial_feature_map.get_mapped_feature_vec_element(0)
sys.stderr.write(str(first_element) + "\n")
result = 1
assert (first_element == result)
my_polynomial_feature_map = PolynomialFeatureMap([I1, I2], [2])
my_polynomial_feature_map.compute_num_terms()
first_element = my_polynomial_feature_map.get_mapped_feature_vec_element(1)
result = np.multiply(I1, I2)
assert (first_element == result).all()
my_polynomial_feature_map = PolynomialFeatureMap([I1, I2], [2, 2, 1, 2])
my_polynomial_feature_map.compute_num_terms()
first_element = my_polynomial_feature_map.get_mapped_feature_vec_element(1)
result = I2
assert (first_element == result).all()
def test_polynomial_feature_map_num_terms():
my_polynomial_feature_map = PolynomialFeatureMap([I1, I2], [1, 2])
my_polynomial_feature_map.compute_num_terms()
sys.stdout = my_polynomial_feature_map.num_terms
assert my_polynomial_feature_map.num_terms == 5
my_polynomial_feature_map = PolynomialFeatureMap([I1, I2], [0])
my_polynomial_feature_map.compute_num_terms()
sys.stdout = my_polynomial_feature_map.num_terms
assert my_polynomial_feature_map.num_terms == 1
my_polynomial_feature_map = PolynomialFeatureMap([I1, I2], [2, 2])
my_polynomial_feature_map.compute_num_terms()
sys.stdout = my_polynomial_feature_map.num_terms
assert my_polynomial_feature_map.num_terms == 3
my_polynomial_feature_map = PolynomialFeatureMap([I1, I2], [2, 1, 2])
my_polynomial_feature_map.compute_num_terms()
sys.stdout = my_polynomial_feature_map.num_terms
assert my_polynomial_feature_map.num_terms == 5
my_polynomial_feature_map = PolynomialFeatureMap([I1, I2, I3], [2])
my_polynomial_feature_map.compute_num_terms()
sys.stdout = my_polynomial_feature_map.num_terms
assert my_polynomial_feature_map.num_terms == 6
my_polynomial_feature_map = PolynomialFeatureMap([I1, I2, I3],
[2, 1, 2, 1, 1])
my_polynomial_feature_map.compute_num_terms()
sys.stdout = my_polynomial_feature_map.num_terms
assert my_polynomial_feature_map.num_terms == 9
def test_polynomial_feature_map():
sys.stderr.write(str(I1)+"\n")
sys.stderr.write(str(I2)+"\n")
my_polynomial_feature_map = PolynomialFeatureMap( [I1,I2],[1,2] )
my_polynomial_feature_map.compute_num_terms()
first_element = my_polynomial_feature_map.get_mapped_feature_vec_element(2)
result = np.multiply(I1,I1)
assert (first_element == result).all()
first_element = my_polynomial_feature_map.get_mapped_feature_vec_element(0)
result = I1
assert (first_element == result).all()
my_polynomial_feature_map = PolynomialFeatureMap( [I1,I2], [0])
my_polynomial_feature_map.compute_num_terms()
first_element = my_polynomial_feature_map.get_mapped_feature_vec_element(0)
sys.stderr.write(str(first_element)+"\n")
result = 1
assert (first_element == result)
my_polynomial_feature_map = PolynomialFeatureMap([I1,I2], [2])
my_polynomial_feature_map.compute_num_terms()
first_element = my_polynomial_feature_map.get_mapped_feature_vec_element(1)
result = np.multiply(I1,I2)
assert (first_element == result).all()
my_polynomial_feature_map = PolynomialFeatureMap([I1,I2], [2,2,1,2])
my_polynomial_feature_map.compute_num_terms()
first_element = my_polynomial_feature_map.get_mapped_feature_vec_element(1)
result = I2
assert (first_element == result).all()
def test_polynomial_feature_map_num_terms():
my_polynomial_feature_map = PolynomialFeatureMap([I1, I2], [1, 2])
my_polynomial_feature_map.compute_num_terms()
sys.stdout=my_polynomial_feature_map.num_terms
assert my_polynomial_feature_map.num_terms == 5
my_polynomial_feature_map = PolynomialFeatureMap( [I1,I2], [0])
my_polynomial_feature_map.compute_num_terms()
sys.stdout=my_polynomial_feature_map.num_terms
assert my_polynomial_feature_map.num_terms == 1
my_polynomial_feature_map = PolynomialFeatureMap([I1,I2], [2,2])
my_polynomial_feature_map.compute_num_terms()
sys.stdout=my_polynomial_feature_map.num_terms
assert my_polynomial_feature_map.num_terms == 3
my_polynomial_feature_map = PolynomialFeatureMap([I1,I2], [2,1,2])
my_polynomial_feature_map.compute_num_terms()
sys.stdout=my_polynomial_feature_map.num_terms
assert my_polynomial_feature_map.num_terms == 5
my_polynomial_feature_map = PolynomialFeatureMap([I1,I2,I3], [2])
my_polynomial_feature_map.compute_num_terms()
sys.stdout=my_polynomial_feature_map.num_terms
assert my_polynomial_feature_map.num_terms == 6
my_polynomial_feature_map = PolynomialFeatureMap([I1,I2,I3], [2,1,2,1,1])
my_polynomial_feature_map.compute_num_terms()
sys.stdout=my_polynomial_feature_map.num_terms
assert my_polynomial_feature_map.num_terms == 9
def segment_lung_with_atlas(input_image, probabilistic_atlases, exponential_parameters):
#TODO: and command line option to this command
"""Segment lung using training labeled data.
Parameters
----------
input_image : float array, shape (L, M, N)
probabilistic_atlases : list of float arrays, shape (L, M, N)
Atlas to use as segmentation prior. Each voxel should have a value in
the interval [0, 1], indicating the probability of the class.
exponential_parameters: Parameters of the exponential likelihood distribution
...
Returns
-------
label_map : array, shape (L, M, N)
Segmented image with labels adhering to CIP conventions
"""
#compute feature vectors for left and right lungs
### TODO: replace with likelihood params for class 0.
length = np.shape(probabilistic_atlases[0])[0]
width = np.shape(probabilistic_atlases[0])[1]
#Define lung area to segment
lungPrior = probabilistic_atlases[0] + probabilistic_atlases[1]
zero_indeces_thresholding = lungPrior < 0.35
lungPriorSlicedialated = lungPrior
lungPriorSlicedialated[zero_indeces_thresholding] = 0.0
ones_indeces_thresholding = lungPrior > 0.34
lungPriorSlicedialated[ones_indeces_thresholding] = 1.0
lungPriorSlicedialated = scipy.ndimage.binary_dilation(lungPriorSlicedialated, \
iterations=2)
scipy.ndimage.morphology.binary_fill_holes(lungPriorSlicedialated, \
structure=None, output=lungPriorSlicedialated, origin=0)
tozero_indeces_intensities = lungPriorSlicedialated < 1
left_lung_distance_map = compute_distance_to_atlas(probabilistic_atlases[0])
right_lung_distance_map = compute_distance_to_atlas(probabilistic_atlases[1])
left_polynomial_feature_map = PolynomialFeatureMap( [input_image, \
left_lung_distance_map],[0,1,2] )
left_polynomial_feature_map.compute_num_terms()
right_polynomial_feature_map = PolynomialFeatureMap( [input_image, \
right_lung_distance_map],[0,1,2] )
right_polynomial_feature_map.compute_num_terms()
#define the weights
#exp(-(alpha(1)*Ival + alpha_(2)*Dval+alpha(3)))^2
# = exp(-(alpha(1)^2*Ival^2 + alpha(1)*Ival*alpha(2)*Dval \
# + alpha(1)*Ival*alpha(3) + alpha(2)*Dval * alpha(1)*Ival
# +alpha(2)*Dval*alpha(2)*Dval+alpha(2)*Dval *alpha(3) \
# + alpha(3)*alpha(1)*Ival+ alpha(3)*alpha(2)*Dval+alpha(3)*alpha(3)))
# = exp(-(alpha(3)*alpha(3) + 2*alpha(1)*alpha(3)*Ival \
# + 2*alpha(2)*alpha(3)*Dval + alpha(1)^2*Ival^2 \
# + 2* alpha(1)*alpha(2)*Ival*Dval + alpha(2)^2*dval^2 ))
#ExpWeightedFeatureMapDensity computations:
#accum = sum_d( \
# self.weights[d]*self.feature_map.get_mapped_feature_vec_element(d))
#exponential_density = np.exp(-self.lamda*accum)*self.lamda
#older weights
#left_weights_temp = [0.002149, -0.002069, 5.258745]
#l_alpha_est_right=[0.001241, -0.025153, 4.609616]
#l_alpha_est_non=[-0.001929, 0.010123, 3.937502]
#
#right_weights_temp = [0.002149, -0.002069, 5.258745]
#r_alpha_est_left=[0.001241, -0.025153, 4.609616]
#r_alpha_est_non=[-0.001929, 0.010123, 3.937502]
#r_alpha_est_left=[0.002500, -0.095894, 6.786622]
#right_weights_temp=[0.001245, 0.066628, 4.269774]
#r_alpha_est_non=[-0.001433, -0.005590, 4.143140]
#newer
# left_weights_temp=[0.002242, 0.002139, 5.305966]
# l_alpha_est_right=[0.001987, -0.054164, 5.415881]
# l_alpha_est_non=[-0.001288, -0.034694, 4.225687]
#
# r_alpha_est_left=[0.002209, -0.094936, 6.731629]
# right_weights_temp=[0.001689, 0.072709, 4.398574]
# r_alpha_est_non=[-0.000816, -0.035418, 4.499488]
#newer, 80 bins instead of 50: left works, right doesnt
#left_weights_temp=[0.002312, 0.002666, 5.806209]
#l_alpha_est_right=[0.001729, -0.029253, 5.383404]
#l_alpha_est_non=[-0.001127, 0.009051, 4.617099]
#
#r_alpha_est_left=[0.001914, -0.060901, 6.623247]
#right_weights_temp=[0.001878, 0.073107, 5.053620]
#r_alpha_est_non=[-0.000779, -0.029794, 5.143489]
#l_alpha_est_right=[0.001816, 0.001857, 5.921779]
#left_weights_temp=[0.002463, 0.118871, 5.827013]
#l_alpha_est_non=[-0.000091, 0.012917, 4.446526]
#right_weights_temp=[0.001594, 0.030955, 5.626165] #this is the source of the problemmmm
#r_alpha_est_left=[0.001946, 0.009239, 5.685427]
#r_alpha_est_non=[-0.000090, 0.014221, 4.432606]
#below working
#alpha_dleft_given_left = [0.002149, -0.002069, 5.258745]
#alpha_dleft_given_right=[0.001241, -0.025153, 4.609616]
#alpha_dleft_given_non=[-0.001929, 0.010123, 3.937502]
##
#alpha_dright_given_right = [0.002149, -0.002069, 5.258745]
#alpha_dright_given_left=[0.001241, -0.025153, 4.609616]
#alpha_dright_given_non=[-0.001929, 0.010123, 3.937502]
alpha_dleft_given_left = exponential_parameters[0]
alpha_dleft_given_right=exponential_parameters[1]
alpha_dleft_given_non=exponential_parameters[2]
#
alpha_dright_given_right = exponential_parameters[3]
alpha_dright_given_left=exponential_parameters[4]
alpha_dright_given_non=exponential_parameters[5]
left_weights = [alpha_dleft_given_left[2]*alpha_dleft_given_left[2], \
2*alpha_dleft_given_left[0]*alpha_dleft_given_left[2], \
2*alpha_dleft_given_left[1]*alpha_dleft_given_left[2], \
alpha_dleft_given_left[0]*alpha_dleft_given_left[0], \
2*alpha_dleft_given_left[0]*alpha_dleft_given_left[1], \
alpha_dleft_given_left[1]*alpha_dleft_given_left[1] ]
left_lambda = 1.0
left_weighted_density = ExpWeightedFeatureMapDensity([\
input_image.astype(np.float),left_lung_distance_map], left_weights, \
left_polynomial_feature_map, left_lambda)
left_likelihood = left_weighted_density.compute()
left_weights_given_right = [alpha_dleft_given_right[2]*alpha_dleft_given_right[2], \
2*alpha_dleft_given_right[0]*alpha_dleft_given_right[2], \
2*alpha_dleft_given_right[1]*alpha_dleft_given_right[2], \
alpha_dleft_given_right[0]*alpha_dleft_given_right[0], \
2*alpha_dleft_given_right[0]*alpha_dleft_given_right[1], \
alpha_dleft_given_right[1]*alpha_dleft_given_right[1] ]
left_given_right_weighted_density = ExpWeightedFeatureMapDensity([\
input_image.astype(np.float),left_lung_distance_map],
left_weights_given_right, left_polynomial_feature_map, left_lambda)
LdIgivenRlung = left_given_right_weighted_density.compute()
left_weights_given_nonlung = [alpha_dleft_given_non[2]*alpha_dleft_given_non[2], \
2*alpha_dleft_given_non[0]*alpha_dleft_given_non[2], \
2*alpha_dleft_given_non[1]*alpha_dleft_given_non[2], \
alpha_dleft_given_non[0]*alpha_dleft_given_non[0], \
2*alpha_dleft_given_non[0]*alpha_dleft_given_non[1], \
alpha_dleft_given_non[1]*alpha_dleft_given_non[1] ]
left_given_nonlung_weighted_density = ExpWeightedFeatureMapDensity([ \
input_image.astype(np.float),left_lung_distance_map], \
left_weights_given_nonlung, left_polynomial_feature_map, left_lambda)
LdIgivenNlung = left_given_nonlung_weighted_density.compute()
right_weights = [alpha_dright_given_right[2]*alpha_dright_given_right[2], \
2*alpha_dright_given_right[0]*alpha_dright_given_right[2], \
2*alpha_dright_given_right[1]*alpha_dright_given_right[2], \
alpha_dright_given_right[0]*alpha_dright_given_right[0], \
2*alpha_dright_given_right[0]*alpha_dright_given_right[1], \
alpha_dright_given_right[1]*alpha_dright_given_right[1] ]
right_lambda = 1.0
right_weighted_density = ExpWeightedFeatureMapDensity([input_image, \
right_lung_distance_map], right_weights, \
right_polynomial_feature_map, right_lambda)
right_likelihood = right_weighted_density.compute()
right_weights_given_left = [alpha_dright_given_left[2]*alpha_dright_given_left[2], \
2*alpha_dright_given_left[0]*alpha_dright_given_left[2], \
2*alpha_dright_given_left[1]*alpha_dright_given_left[2], \
alpha_dright_given_left[0]*alpha_dright_given_left[0], \
2*alpha_dright_given_left[0]*alpha_dright_given_left[1], \
alpha_dright_given_left[1]*alpha_dright_given_left[1] ]
right_lambda = 1.0
right_given_leftlung_weighted_density = ExpWeightedFeatureMapDensity( \
[input_image,right_lung_distance_map], right_weights_given_left, \
right_polynomial_feature_map, right_lambda)
RdIgivenLlung = right_given_leftlung_weighted_density.compute()
right_weights_given_non = [alpha_dright_given_non[2]*alpha_dright_given_non[2], \
2*alpha_dright_given_non[0]*alpha_dright_given_non[2], \
2*alpha_dright_given_non[1]*alpha_dright_given_non[2], \
alpha_dright_given_non[0]*alpha_dright_given_non[0], \
2*alpha_dright_given_non[0]*alpha_dright_given_non[1], \
alpha_dright_given_non[1]*alpha_dright_given_non[1] ]
right_lambda = 1.0
right_given_nonlung_weighted_density = ExpWeightedFeatureMapDensity( \
[input_image,right_lung_distance_map], right_weights_given_non, \
right_polynomial_feature_map, right_lambda)
RdIgivenNlung = right_given_nonlung_weighted_density.compute()
notLungPrior = np.ones((length, width,np.shape( \
probabilistic_atlases[0])[2])).astype(np.float)
notLungPrior = notLungPrior - np.add(probabilistic_atlases[0], \
probabilistic_atlases[1]);
p_I_dleft = np.add(np.multiply(left_likelihood.astype(np.float), \
probabilistic_atlases[0].astype(np.float)),np.multiply( \
LdIgivenRlung.astype(np.float),probabilistic_atlases[1].astype( \
np.float)),np.multiply(LdIgivenNlung.astype(np.float), \
notLungPrior.astype(np.float)))
p_I_dright = np.add(np.multiply(RdIgivenLlung.astype(np.float), \
probabilistic_atlases[0].astype(np.float)),np.multiply( \
right_likelihood.astype(np.float), \
probabilistic_atlases[1].astype(np.float)),np.multiply( \
RdIgivenNlung.astype(np.float),notLungPrior.astype(np.float)))
zero_indeces = (p_I_dleft == 0)
p_I_dleft[zero_indeces] = 0.000000000000000000000001
zero_indeces2 = (p_I_dright == 0)
p_I_dright[zero_indeces2] = 0.000000000000000000000001
left_likelihood[tozero_indeces_intensities]=0
right_likelihood[tozero_indeces_intensities]=0
#segment given feature vectors
segmented_labels = segment_chest_with_atlas([left_likelihood.astype( \
np.float), right_likelihood.astype(np.float)], probabilistic_atlases, \
[p_I_dleft.astype(np.float), p_I_dright.astype(np.float)])
return segmented_labels
def segment_lung_with_atlas(input_image, probabilistic_atlases,
exponential_parameters):
#TODO: and command line option to this command
"""Segment lung using training labeled data.
Parameters
----------
input_image : float array, shape (L, M, N)
probabilistic_atlases : list of float arrays, shape (L, M, N)
Atlas to use as segmentation prior. Each voxel should have a value in
the interval [0, 1], indicating the probability of the class.
exponential_parameters: Parameters of the exponential likelihood distribution
...
Returns
-------
label_map : array, shape (L, M, N)
Segmented image with labels adhering to CIP conventions
"""
#compute feature vectors for left and right lungs
### TODO: replace with likelihood params for class 0.
length = np.shape(probabilistic_atlases[0])[0]
width = np.shape(probabilistic_atlases[0])[1]
#Define lung area to segment
lungPrior = probabilistic_atlases[0] + probabilistic_atlases[1]
zero_indeces_thresholding = lungPrior < 0.35
lungPriorSlicedialated = lungPrior
lungPriorSlicedialated[zero_indeces_thresholding] = 0.0
ones_indeces_thresholding = lungPrior > 0.34
lungPriorSlicedialated[ones_indeces_thresholding] = 1.0
lungPriorSlicedialated = scipy.ndimage.binary_dilation(lungPriorSlicedialated, \
iterations=2)
scipy.ndimage.morphology.binary_fill_holes(lungPriorSlicedialated, \
structure=None, output=lungPriorSlicedialated, origin=0)
tozero_indeces_intensities = lungPriorSlicedialated < 1
left_lung_distance_map = compute_distance_to_atlas(
probabilistic_atlases[0])
right_lung_distance_map = compute_distance_to_atlas(
probabilistic_atlases[1])
left_polynomial_feature_map = PolynomialFeatureMap( [input_image, \
left_lung_distance_map],[0,1,2] )
left_polynomial_feature_map.compute_num_terms()
right_polynomial_feature_map = PolynomialFeatureMap( [input_image, \
right_lung_distance_map],[0,1,2] )
right_polynomial_feature_map.compute_num_terms()
#define the weights
#exp(-(alpha(1)*Ival + alpha_(2)*Dval+alpha(3)))^2
# = exp(-(alpha(1)^2*Ival^2 + alpha(1)*Ival*alpha(2)*Dval \
# + alpha(1)*Ival*alpha(3) + alpha(2)*Dval * alpha(1)*Ival
# +alpha(2)*Dval*alpha(2)*Dval+alpha(2)*Dval *alpha(3) \
# + alpha(3)*alpha(1)*Ival+ alpha(3)*alpha(2)*Dval+alpha(3)*alpha(3)))
# = exp(-(alpha(3)*alpha(3) + 2*alpha(1)*alpha(3)*Ival \
# + 2*alpha(2)*alpha(3)*Dval + alpha(1)^2*Ival^2 \
# + 2* alpha(1)*alpha(2)*Ival*Dval + alpha(2)^2*dval^2 ))
#ExpWeightedFeatureMapDensity computations:
#accum = sum_d( \
# self.weights[d]*self.feature_map.get_mapped_feature_vec_element(d))
#exponential_density = np.exp(-self.lamda*accum)*self.lamda
#older weights
#left_weights_temp = [0.002149, -0.002069, 5.258745]
#l_alpha_est_right=[0.001241, -0.025153, 4.609616]
#l_alpha_est_non=[-0.001929, 0.010123, 3.937502]
#
#right_weights_temp = [0.002149, -0.002069, 5.258745]
#r_alpha_est_left=[0.001241, -0.025153, 4.609616]
#r_alpha_est_non=[-0.001929, 0.010123, 3.937502]
#r_alpha_est_left=[0.002500, -0.095894, 6.786622]
#right_weights_temp=[0.001245, 0.066628, 4.269774]
#r_alpha_est_non=[-0.001433, -0.005590, 4.143140]
#newer
# left_weights_temp=[0.002242, 0.002139, 5.305966]
# l_alpha_est_right=[0.001987, -0.054164, 5.415881]
# l_alpha_est_non=[-0.001288, -0.034694, 4.225687]
#
# r_alpha_est_left=[0.002209, -0.094936, 6.731629]
# right_weights_temp=[0.001689, 0.072709, 4.398574]
# r_alpha_est_non=[-0.000816, -0.035418, 4.499488]
#newer, 80 bins instead of 50: left works, right doesnt
#left_weights_temp=[0.002312, 0.002666, 5.806209]
#l_alpha_est_right=[0.001729, -0.029253, 5.383404]
#l_alpha_est_non=[-0.001127, 0.009051, 4.617099]
#
#r_alpha_est_left=[0.001914, -0.060901, 6.623247]
#right_weights_temp=[0.001878, 0.073107, 5.053620]
#r_alpha_est_non=[-0.000779, -0.029794, 5.143489]
#l_alpha_est_right=[0.001816, 0.001857, 5.921779]
#left_weights_temp=[0.002463, 0.118871, 5.827013]
#l_alpha_est_non=[-0.000091, 0.012917, 4.446526]
#right_weights_temp=[0.001594, 0.030955, 5.626165] #this is the source of the problemmmm
#r_alpha_est_left=[0.001946, 0.009239, 5.685427]
#r_alpha_est_non=[-0.000090, 0.014221, 4.432606]
#below working
#alpha_dleft_given_left = [0.002149, -0.002069, 5.258745]
#alpha_dleft_given_right=[0.001241, -0.025153, 4.609616]
#alpha_dleft_given_non=[-0.001929, 0.010123, 3.937502]
##
#alpha_dright_given_right = [0.002149, -0.002069, 5.258745]
#alpha_dright_given_left=[0.001241, -0.025153, 4.609616]
#alpha_dright_given_non=[-0.001929, 0.010123, 3.937502]
alpha_dleft_given_left = exponential_parameters[0]
alpha_dleft_given_right = exponential_parameters[1]
alpha_dleft_given_non = exponential_parameters[2]
#
alpha_dright_given_right = exponential_parameters[3]
alpha_dright_given_left = exponential_parameters[4]
alpha_dright_given_non = exponential_parameters[5]
left_weights = [alpha_dleft_given_left[2]*alpha_dleft_given_left[2], \
2*alpha_dleft_given_left[0]*alpha_dleft_given_left[2], \
2*alpha_dleft_given_left[1]*alpha_dleft_given_left[2], \
alpha_dleft_given_left[0]*alpha_dleft_given_left[0], \
2*alpha_dleft_given_left[0]*alpha_dleft_given_left[1], \
alpha_dleft_given_left[1]*alpha_dleft_given_left[1] ]
left_lambda = 1.0
left_weighted_density = ExpWeightedFeatureMapDensity([\
input_image.astype(np.float),left_lung_distance_map], left_weights, \
left_polynomial_feature_map, left_lambda)
left_likelihood = left_weighted_density.compute()
left_weights_given_right = [alpha_dleft_given_right[2]*alpha_dleft_given_right[2], \
2*alpha_dleft_given_right[0]*alpha_dleft_given_right[2], \
2*alpha_dleft_given_right[1]*alpha_dleft_given_right[2], \
alpha_dleft_given_right[0]*alpha_dleft_given_right[0], \
2*alpha_dleft_given_right[0]*alpha_dleft_given_right[1], \
alpha_dleft_given_right[1]*alpha_dleft_given_right[1] ]
left_given_right_weighted_density = ExpWeightedFeatureMapDensity([\
input_image.astype(np.float),left_lung_distance_map],
left_weights_given_right, left_polynomial_feature_map, left_lambda)
LdIgivenRlung = left_given_right_weighted_density.compute()
left_weights_given_nonlung = [alpha_dleft_given_non[2]*alpha_dleft_given_non[2], \
2*alpha_dleft_given_non[0]*alpha_dleft_given_non[2], \
2*alpha_dleft_given_non[1]*alpha_dleft_given_non[2], \
alpha_dleft_given_non[0]*alpha_dleft_given_non[0], \
2*alpha_dleft_given_non[0]*alpha_dleft_given_non[1], \
alpha_dleft_given_non[1]*alpha_dleft_given_non[1] ]
left_given_nonlung_weighted_density = ExpWeightedFeatureMapDensity([ \
input_image.astype(np.float),left_lung_distance_map], \
left_weights_given_nonlung, left_polynomial_feature_map, left_lambda)
LdIgivenNlung = left_given_nonlung_weighted_density.compute()
right_weights = [alpha_dright_given_right[2]*alpha_dright_given_right[2], \
2*alpha_dright_given_right[0]*alpha_dright_given_right[2], \
2*alpha_dright_given_right[1]*alpha_dright_given_right[2], \
alpha_dright_given_right[0]*alpha_dright_given_right[0], \
2*alpha_dright_given_right[0]*alpha_dright_given_right[1], \
alpha_dright_given_right[1]*alpha_dright_given_right[1] ]
right_lambda = 1.0
right_weighted_density = ExpWeightedFeatureMapDensity([input_image, \
right_lung_distance_map], right_weights, \
right_polynomial_feature_map, right_lambda)
right_likelihood = right_weighted_density.compute()
right_weights_given_left = [alpha_dright_given_left[2]*alpha_dright_given_left[2], \
2*alpha_dright_given_left[0]*alpha_dright_given_left[2], \
2*alpha_dright_given_left[1]*alpha_dright_given_left[2], \
alpha_dright_given_left[0]*alpha_dright_given_left[0], \
2*alpha_dright_given_left[0]*alpha_dright_given_left[1], \
alpha_dright_given_left[1]*alpha_dright_given_left[1] ]
right_lambda = 1.0
right_given_leftlung_weighted_density = ExpWeightedFeatureMapDensity( \
[input_image,right_lung_distance_map], right_weights_given_left, \
right_polynomial_feature_map, right_lambda)
RdIgivenLlung = right_given_leftlung_weighted_density.compute()
right_weights_given_non = [alpha_dright_given_non[2]*alpha_dright_given_non[2], \
2*alpha_dright_given_non[0]*alpha_dright_given_non[2], \
2*alpha_dright_given_non[1]*alpha_dright_given_non[2], \
alpha_dright_given_non[0]*alpha_dright_given_non[0], \
2*alpha_dright_given_non[0]*alpha_dright_given_non[1], \
alpha_dright_given_non[1]*alpha_dright_given_non[1] ]
right_lambda = 1.0
right_given_nonlung_weighted_density = ExpWeightedFeatureMapDensity( \
[input_image,right_lung_distance_map], right_weights_given_non, \
right_polynomial_feature_map, right_lambda)
RdIgivenNlung = right_given_nonlung_weighted_density.compute()
notLungPrior = np.ones((length, width,np.shape( \
probabilistic_atlases[0])[2])).astype(np.float)
notLungPrior = notLungPrior - np.add(probabilistic_atlases[0], \
probabilistic_atlases[1])
p_I_dleft = np.add(np.multiply(left_likelihood.astype(np.float), \
probabilistic_atlases[0].astype(np.float)),np.multiply( \
LdIgivenRlung.astype(np.float),probabilistic_atlases[1].astype( \
np.float)),np.multiply(LdIgivenNlung.astype(np.float), \
notLungPrior.astype(np.float)))
p_I_dright = np.add(np.multiply(RdIgivenLlung.astype(np.float), \
probabilistic_atlases[0].astype(np.float)),np.multiply( \
right_likelihood.astype(np.float), \
probabilistic_atlases[1].astype(np.float)),np.multiply( \
RdIgivenNlung.astype(np.float),notLungPrior.astype(np.float)))
zero_indeces = (p_I_dleft == 0)
p_I_dleft[zero_indeces] = 0.000000000000000000000001
zero_indeces2 = (p_I_dright == 0)
p_I_dright[zero_indeces2] = 0.000000000000000000000001
left_likelihood[tozero_indeces_intensities] = 0
right_likelihood[tozero_indeces_intensities] = 0
#segment given feature vectors
segmented_labels = segment_chest_with_atlas([left_likelihood.astype( \
np.float), right_likelihood.astype(np.float)], probabilistic_atlases, \
[p_I_dleft.astype(np.float), p_I_dright.astype(np.float)])
return segmented_labels
