def testInversion_invertible():
displacement_clean=tf.create_invertible_displacement_field(256, 256, 0.5, 8)
detJacobian=rcommon.computeJacobianField(displacement_clean)
plt.figure()
plt.imshow(detJacobian)
print 'Range:', detJacobian.min(), detJacobian.max()
X1,X0=np.mgrid[0:displacement_clean.shape[0], 0:displacement_clean.shape[1]]
CS=plt.contour(X0,X1,detJacobian,levels=[0.0], colors='b')
plt.clabel(CS, inline=1, fontsize=10)
plt.title('det(J(displacement))')
displacement=displacement_clean+np.random.normal(0.0, 1.1, displacement_clean.shape)
#displacement=np.array(displacement_clean)
#inverse=rcommon.invert_vector_field_fixed_point(displacement, 100, 1e-7)
#inverse=np.array(tf.invert_vector_field(displacement, 0.1, 100, 1e-7))
lambdaParam=5.0
#########Jacobi##########
inverse=np.array(tf.invert_vector_field(displacement, lambdaParam, 100, 1e-6))
residual, stats=tf.compose_vector_fields(displacement_clean, inverse)
residual=np.array(residual)
print 'Jacobi. Max:',stats[0], '. Mean:',stats[1],'Std:',stats[2]
[d,invd,res, detJ]=rcommon.plotDiffeomorphism(displacement, inverse, residual, 'Jacobi', 7)
#########Fixed point######
inverse=np.array(tf.invert_vector_field_fixed_point(displacement, 100, 1e-6))
residual, stats=tf.compose_vector_fields(displacement_clean, inverse)
residual=np.array(residual)
print 'Fixed point. Max:',stats[0], '. Mean:',stats[1],'Std:',stats[2]
[d,invd,res, detJ]=rcommon.plotDiffeomorphism(displacement, inverse, residual, 'Fixed point', 7)
#########TV-L2###########
inverse=np.array(tf.invert_vector_field_tv_l2(displacement, lambdaParam, 3000, 1e-6))
residual, stats=tf.compose_vector_fields(displacement_clean, inverse)
residual=np.array(residual)
print 'TV-L2. Max:',stats[0], '. Mean:',stats[1],'Std:',stats[2]
[d,invd,res, detJ]=rcommon.plotDiffeomorphism(displacement, inverse, residual, 'TV-L2', 7)
def testEstimateMonomodalDiffeomorphicField2DMultiScale(lambdaParam):
fname0='IBSR_01_to_02.nii.gz'
fname1='data/t1/IBSR18/IBSR_02/IBSR_02_ana_strip.nii.gz'
nib_moving = nib.load(fname0)
nib_fixed= nib.load(fname1)
moving=nib_moving.get_data().squeeze().astype(np.float64)
fixed=nib_fixed.get_data().squeeze().astype(np.float64)
moving=np.copy(moving, order='C')
fixed=np.copy(fixed, order='C')
sl=moving.shape
sr=fixed.shape
level=5
#---sagital---
moving=moving[sl[0]//2,:,:].copy()
fixed=fixed[sr[0]//2,:,:].copy()
#---coronal---
#moving=moving[:,sl[1]//2,:].copy()
#fixed=fixed[:,sr[1]//2,:].copy()
#---axial---
#moving=moving[:,:,sl[2]//2].copy()
#fixed=fixed[:,:,sr[2]//2].copy()
maskMoving=moving>0
maskFixed=fixed>0
movingPyramid=[img for img in rcommon.pyramid_gaussian_2D(moving, level, maskMoving)]
fixedPyramid=[img for img in rcommon.pyramid_gaussian_2D(fixed, level, maskFixed)]
rcommon.plotOverlaidPyramids(movingPyramid, fixedPyramid)
displacementList=[]
maxIter=200
displacement, inverse=estimateMonomodalDiffeomorphicField2DMultiScale(movingPyramid, fixedPyramid, lambdaParam, maxIter, 0,displacementList)
residual=tf.compose_vector_fields(displacement, inverse)
warpPyramid=[rcommon.warpImage(movingPyramid[i], displacementList[i]) for i in range(level+1)]
rcommon.plotOverlaidPyramids(warpPyramid, fixedPyramid)
rcommon.overlayImages(warpPyramid[0], fixedPyramid[0])
rcommon.plotDiffeomorphism(displacement, inverse, residual)
def testCircleToCMonomodalSyNEM(lambdaParam, maxOuterIter):
fname0='data/circle.png'
#fname0='data/C_trans.png'
fname1='data/C.png'
nib_moving=plt.imread(fname0)
nib_fixed=plt.imread(fname1)
moving=nib_moving[:,:,0]
fixed=nib_fixed[:,:,1]
moving=(moving-moving.min())/(moving.max() - moving.min())
fixed=(fixed-fixed.min())/(fixed.max() - fixed.min())
level=3
maskMoving=moving>0
maskFixed=fixed>0
movingPyramid=[img for img in rcommon.pyramid_gaussian_2D(moving, level, maskMoving)]
fixedPyramid=[img for img in rcommon.pyramid_gaussian_2D(fixed, level, maskFixed)]
rcommon.plotOverlaidPyramids(movingPyramid, fixedPyramid)
displacementList=[]
displacement, dinv=estimateMonomodalSyNField2DMultiScale(movingPyramid, fixedPyramid, lambdaParam, maxOuterIter, 0,displacementList)
inverse=np.array(tf.invert_vector_field(displacement, 0.75, 300, 1e-7))
residual, stats=tf.compose_vector_fields(displacement, inverse)
residual=np.array(residual)
warpPyramid=[rcommon.warpImage(movingPyramid[i], displacementList[i]) for i in range(level+1)]
rcommon.plotOverlaidPyramids(warpPyramid, fixedPyramid)
rcommon.overlayImages(warpPyramid[0], fixedPyramid[0])
rcommon.plotDiffeomorphism(displacement, inverse, residual, '',7)
def testCircleToCMonomodalSyNEM(lambdaParam, maxOuterIter):
fname0 = 'data/circle.png'
#fname0='data/C_trans.png'
fname1 = 'data/C.png'
nib_moving = plt.imread(fname0)
nib_fixed = plt.imread(fname1)
moving = nib_moving[:, :, 0]
fixed = nib_fixed[:, :, 1]
moving = (moving - moving.min()) / (moving.max() - moving.min())
fixed = (fixed - fixed.min()) / (fixed.max() - fixed.min())
level = 3
maskMoving = moving > 0
maskFixed = fixed > 0
movingPyramid = [
img for img in rcommon.pyramid_gaussian_2D(moving, level, maskMoving)
]
fixedPyramid = [
img for img in rcommon.pyramid_gaussian_2D(fixed, level, maskFixed)
]
rcommon.plotOverlaidPyramids(movingPyramid, fixedPyramid)
displacementList = []
displacement, dinv = estimateMonomodalSyNField2DMultiScale(
movingPyramid, fixedPyramid, lambdaParam, maxOuterIter, 0,
displacementList)
inverse = np.array(tf.invert_vector_field(displacement, 0.75, 300, 1e-7))
residual, stats = tf.compose_vector_fields(displacement, inverse)
residual = np.array(residual)
warpPyramid = [
rcommon.warpImage(movingPyramid[i], displacementList[i])
for i in range(level + 1)
]
rcommon.plotOverlaidPyramids(warpPyramid, fixedPyramid)
rcommon.overlayImages(warpPyramid[0], fixedPyramid[0])
rcommon.plotDiffeomorphism(displacement, inverse, residual, '', 7)
def testCircleToCMonomodalDiffeomorphic(lambdaParam):
import numpy as np
import tensorFieldUtils as tf
import matplotlib.pyplot as plt
import registrationCommon as rcommon
fname0='data/circle.png'
#fname0='data/C_trans.png'
fname1='data/C.png'
circleToCDisplacementName='circleToCDisplacement.npy'
circleToCDisplacementInverseName='circleToCDisplacementInverse.npy'
nib_moving=plt.imread(fname0)
nib_fixed=plt.imread(fname1)
moving=nib_moving[:,:,0]
fixed=nib_fixed[:,:,1]
moving=(moving-moving.min())/(moving.max() - moving.min())
fixed=(fixed-fixed.min())/(fixed.max() - fixed.min())
level=3
maskMoving=moving>0
maskFixed=fixed>0
movingPyramid=[img for img in rcommon.pyramid_gaussian_2D(moving, level, np.ones_like(maskMoving))]
fixedPyramid=[img for img in rcommon.pyramid_gaussian_2D(fixed, level, np.ones_like(maskFixed))]
rcommon.plotOverlaidPyramids(movingPyramid, fixedPyramid)
displacementList=[]
maxOuterIter=[10,50,100,100,100,100,100,100,100]
if(os.path.exists(circleToCDisplacementName)):
displacement=np.load(circleToCDisplacementName)
inverse=np.load(circleToCDisplacementInverseName)
else:
displacement, inverse=estimateMonomodalDiffeomorphicField2DMultiScale(movingPyramid, fixedPyramid, lambdaParam, maxOuterIter, 0,displacementList)
np.save(circleToCDisplacementName, displacement)
np.save(circleToCDisplacementInverseName, inverse)
X1,X0=np.mgrid[0:displacement.shape[0], 0:displacement.shape[1]]
detJacobian=rcommon.computeJacobianField(displacement)
plt.figure()
plt.imshow(detJacobian)
CS=plt.contour(X0,X1,detJacobian,levels=[0.0], colors='b')
plt.clabel(CS, inline=1, fontsize=10)
plt.title('det(J(displacement))')
print 'J range:', '[', detJacobian.min(), detJacobian.max(),']'
#directInverse=np.array(tf.invert_vector_field(displacement, 0.5, 1000, 1e-7))
directInverse=np.array(tf.invert_vector_field_fixed_point(displacement, 100, 1e-7))
detJacobianInverse=rcommon.computeJacobianField(directInverse)
plt.figure()
plt.imshow(detJacobianInverse)
CS=plt.contour(X0,X1,detJacobianInverse, levels=[0.0],colors='w')
plt.clabel(CS, inline=1, fontsize=10)
plt.title('det(J(displacement^-1))')
print 'J^-1 range:', '[', detJacobianInverse.min(), detJacobianInverse.max(),']'
#directInverse=rcommon.invert_vector_field_fixed_point(displacement, 1000, 1e-7)
residual, stats=tf.compose_vector_fields(displacement, inverse)
residual=np.array(residual)
directResidual,stats=tf.compose_vector_fields(displacement, directInverse)
directResidual=np.array(directResidual)
# warpPyramid=[rcommon.warpImage(movingPyramid[i], displacementList[i]) for i in range(level+1)]
# rcommon.plotOverlaidPyramids(warpPyramid, fixedPyramid)
# rcommon.overlayImages(warpPyramid[0], fixedPyramid[0])
rcommon.plotDiffeomorphism(displacement, inverse, residual, 'inv-joint', 7)
rcommon.plotDiffeomorphism(displacement, directInverse, directResidual, 'inv-direct', 7)
tf.write_double_buffer(displacement.reshape(-1), 'displacement.bin')
def test_optimizer_monomodal_2d():
r'''
Classical Circle-To-C experiment for 2D Monomodal registration
'''
fname_moving = 'data/circle.png'
fname_fixed = 'data/C.png'
moving = plt.imread(fname_moving)
fixed = plt.imread(fname_fixed)
moving = moving[:, :, 0].astype(np.float64)
fixed = fixed[:, :, 0].astype(np.float64)
moving = np.copy(moving, order='C')
fixed = np.copy(fixed, order='C')
moving = (moving - moving.min()) / (moving.max() - moving.min())
fixed = (fixed - fixed.min()) / (fixed.max() - fixed.min())
################Configure and run the Optimizer#####################
max_iter = [i for i in [20, 100, 100, 100]]
similarity_metric = SSDMetric(2, {
'symmetric': True,
'lambda': 5.0,
'stepType': SSDMetric.GAUSS_SEIDEL_STEP
})
optimizer_parameters = {
'max_iter': max_iter,
'inversion_iter': 40,
'inversion_tolerance': 1e-3,
'report_status': True
}
update_rule = UpdateRule.Composition()
registration_optimizer = SymmetricRegistrationOptimizer(
fixed, moving, None, None, similarity_metric, update_rule,
optimizer_parameters)
registration_optimizer.optimize()
#######################show results#################################
displacement = registration_optimizer.get_forward()
direct_inverse = registration_optimizer.get_backward()
moving_to_fixed = np.array(tf.warp_image(moving, displacement))
fixed_to_moving = np.array(tf.warp_image(fixed, direct_inverse))
rcommon.overlayImages(moving_to_fixed, fixed, True)
rcommon.overlayImages(fixed_to_moving, moving, True)
direct_residual, stats = tf.compose_vector_fields(displacement,
direct_inverse)
direct_residual = np.array(direct_residual)
rcommon.plotDiffeomorphism(displacement, direct_inverse, direct_residual,
'inv-direct', 7)
def testInversion_invertible():
displacement_clean = tf.create_invertible_displacement_field(
256, 256, 0.5, 8)
detJacobian = rcommon.computeJacobianField(displacement_clean)
plt.figure()
plt.imshow(detJacobian)
print 'Range:', detJacobian.min(), detJacobian.max()
X1, X0 = np.mgrid[0:displacement_clean.shape[0],
0:displacement_clean.shape[1]]
CS = plt.contour(X0, X1, detJacobian, levels=[0.0], colors='b')
plt.clabel(CS, inline=1, fontsize=10)
plt.title('det(J(displacement))')
displacement = displacement_clean + np.random.normal(
0.0, 1.1, displacement_clean.shape)
#displacement=np.array(displacement_clean)
#inverse=rcommon.invert_vector_field_fixed_point(displacement, 100, 1e-7)
#inverse=np.array(tf.invert_vector_field(displacement, 0.1, 100, 1e-7))
lambdaParam = 5.0
#########Jacobi##########
inverse = np.array(
tf.invert_vector_field(displacement, lambdaParam, 100, 1e-6))
residual, stats = tf.compose_vector_fields(displacement_clean, inverse)
residual = np.array(residual)
print 'Jacobi. Max:', stats[0], '. Mean:', stats[1], 'Std:', stats[2]
[d, invd, res, detJ] = rcommon.plotDiffeomorphism(displacement, inverse,
residual, 'Jacobi', 7)
#########Fixed point######
inverse = np.array(
tf.invert_vector_field_fixed_point(displacement, 100, 1e-6))
residual, stats = tf.compose_vector_fields(displacement_clean, inverse)
residual = np.array(residual)
print 'Fixed point. Max:', stats[0], '. Mean:', stats[1], 'Std:', stats[2]
[d, invd, res,
detJ] = rcommon.plotDiffeomorphism(displacement, inverse, residual,
'Fixed point', 7)
#########TV-L2###########
inverse = np.array(
tf.invert_vector_field_tv_l2(displacement, lambdaParam, 3000, 1e-6))
residual, stats = tf.compose_vector_fields(displacement_clean, inverse)
residual = np.array(residual)
print 'TV-L2. Max:', stats[0], '. Mean:', stats[1], 'Std:', stats[2]
[d, invd, res, detJ] = rcommon.plotDiffeomorphism(displacement, inverse,
residual, 'TV-L2', 7)
def testEstimateMonomodalDiffeomorphicField2DMultiScale(lambdaParam):
fname0 = 'IBSR_01_to_02.nii.gz'
fname1 = 'data/t1/IBSR18/IBSR_02/IBSR_02_ana_strip.nii.gz'
nib_moving = nib.load(fname0)
nib_fixed = nib.load(fname1)
moving = nib_moving.get_data().squeeze().astype(np.float64)
fixed = nib_fixed.get_data().squeeze().astype(np.float64)
moving = np.copy(moving, order='C')
fixed = np.copy(fixed, order='C')
sl = moving.shape
sr = fixed.shape
level = 5
#---sagital---
moving = moving[sl[0] // 2, :, :].copy()
fixed = fixed[sr[0] // 2, :, :].copy()
#---coronal---
#moving=moving[:,sl[1]//2,:].copy()
#fixed=fixed[:,sr[1]//2,:].copy()
#---axial---
#moving=moving[:,:,sl[2]//2].copy()
#fixed=fixed[:,:,sr[2]//2].copy()
maskMoving = moving > 0
maskFixed = fixed > 0
movingPyramid = [
img for img in rcommon.pyramid_gaussian_2D(moving, level, maskMoving)
]
fixedPyramid = [
img for img in rcommon.pyramid_gaussian_2D(fixed, level, maskFixed)
]
rcommon.plotOverlaidPyramids(movingPyramid, fixedPyramid)
displacementList = []
maxIter = 200
displacement, inverse = estimateMonomodalDiffeomorphicField2DMultiScale(
movingPyramid, fixedPyramid, lambdaParam, maxIter, 0, displacementList)
residual = tf.compose_vector_fields(displacement, inverse)
warpPyramid = [
rcommon.warpImage(movingPyramid[i], displacementList[i])
for i in range(level + 1)
]
rcommon.plotOverlaidPyramids(warpPyramid, fixedPyramid)
rcommon.overlayImages(warpPyramid[0], fixedPyramid[0])
rcommon.plotDiffeomorphism(displacement, inverse, residual)
def test_optimizer_monomodal_2d():
r'''
Classical Circle-To-C experiment for 2D Monomodal registration
'''
fname_moving = 'data/circle.png'
fname_fixed = 'data/C.png'
moving = plt.imread(fname_moving)
fixed = plt.imread(fname_fixed)
moving = moving[:, :, 0].astype(np.float64)
fixed = fixed[:, :, 0].astype(np.float64)
moving = np.copy(moving, order = 'C')
fixed = np.copy(fixed, order = 'C')
moving = (moving-moving.min())/(moving.max() - moving.min())
fixed = (fixed-fixed.min())/(fixed.max() - fixed.min())
################Configure and run the Optimizer#####################
max_iter = [i for i in [20, 100, 100, 100]]
similarity_metric = SSDMetric(2, {'symmetric':True,
'lambda':5.0,
'stepType':SSDMetric.GAUSS_SEIDEL_STEP})
optimizer_parameters = {
'max_iter':max_iter,
'inversion_iter':40,
'inversion_tolerance':1e-3,
'report_status':True}
update_rule = UpdateRule.Composition()
registration_optimizer = SymmetricRegistrationOptimizer(fixed, moving,
None, None,
similarity_metric,
update_rule, optimizer_parameters)
registration_optimizer.optimize()
#######################show results#################################
displacement = registration_optimizer.get_forward()
direct_inverse = registration_optimizer.get_backward()
moving_to_fixed = np.array(tf.warp_image(moving, displacement))
fixed_to_moving = np.array(tf.warp_image(fixed, direct_inverse))
rcommon.overlayImages(moving_to_fixed, fixed, True)
rcommon.overlayImages(fixed_to_moving, moving, True)
direct_residual, stats = tf.compose_vector_fields(displacement,
direct_inverse)
direct_residual = np.array(direct_residual)
rcommon.plotDiffeomorphism(displacement, direct_inverse, direct_residual,
'inv-direct', 7)
def test_optimizer_monomodal_2d():
r"""
Classical Circle-To-C experiment for 2D Monomodal registration
"""
fname_moving = "data/circle.png"
fname_fixed = "data/C.png"
nib_moving = plt.imread(fname_moving)
nib_fixed = plt.imread(fname_fixed)
moving = nib_moving[:, :, 0].astype(np.float64)
fixed = nib_fixed[:, :, 1].astype(np.float64)
moving = np.copy(moving, order="C")
fixed = np.copy(fixed, order="C")
moving = (moving - moving.min()) / (moving.max() - moving.min())
fixed = (fixed - fixed.min()) / (fixed.max() - fixed.min())
################Configure and run the Optimizer#####################
max_iter = [i for i in [25, 100, 100, 100]]
similarity_metric = SSDMetric({"lambda": 5.0, "max_inner_iter": 50, "step_type": SSDMetric.GAUSS_SEIDEL_STEP})
update_rule = UpdateRule.Composition()
optimizer_parameters = {
"max_iter": max_iter,
"inversion_iter": 40,
"inversion_tolerance": 1e-3,
"report_status": True,
}
registration_optimizer = AsymmetricRegistrationOptimizer(
fixed, moving, None, None, similarity_metric, update_rule, optimizer_parameters
)
registration_optimizer.optimize()
#######################show results#################################
displacement = registration_optimizer.get_forward()
direct_inverse = registration_optimizer.get_backward()
moving_to_fixed = np.array(tf.warp_image(moving, displacement))
fixed_to_moving = np.array(tf.warp_image(fixed, direct_inverse))
rcommon.overlayImages(moving_to_fixed, fixed, True)
rcommon.overlayImages(fixed_to_moving, moving, True)
direct_residual, stats = tf.compose_vector_fields(displacement, direct_inverse)
direct_residual = np.array(direct_residual)
rcommon.plotDiffeomorphism(displacement, direct_inverse, direct_residual, "inv-direct", 7)
def testInversion(lambdaParam):
fname0='data/circle.png'
fname1='data/C.png'
circleToCDisplacementName='circleToCDisplacement.npy'
circleToCDisplacementInverseName='circleToCDisplacementInverse.npy'
nib_moving=plt.imread(fname0)
nib_fixed=plt.imread(fname1)
moving=nib_moving[:,:,0]
fixed=nib_fixed[:,:,1]
moving=(moving-moving.min())/(moving.max() - moving.min())
fixed=(fixed-fixed.min())/(fixed.max() - fixed.min())
level=3
maskMoving=moving>0
maskFixed=fixed>0
movingPyramid=[img for img in rcommon.pyramid_gaussian_2D(moving, level, np.ones_like(maskMoving))]
fixedPyramid=[img for img in rcommon.pyramid_gaussian_2D(fixed, level, np.ones_like(maskFixed))]
rcommon.plotOverlaidPyramids(movingPyramid, fixedPyramid)
displacementList=[]
maxOuterIter=[10,50,100,100,100,100,100,100,100]
if(os.path.exists(circleToCDisplacementName)):
displacement=np.load(circleToCDisplacementName)
inverse=np.load(circleToCDisplacementInverseName)
else:
displacement, inverse=estimateMonomodalDiffeomorphicField2DMultiScale(movingPyramid, fixedPyramid, lambdaParam, maxOuterIter, 0,displacementList)
np.save(circleToCDisplacementName, displacement)
np.save(circleToCDisplacementInverseName, inverse)
print 'vector field exponential'
expd, invexpd=tf.vector_field_exponential(displacement, True)
print 'vector field inversion'
directInverse=tf.invert_vector_field(displacement, 1.0, 10000, 1e-7)
print 'vector field inversion'
directExpInverse=tf.invert_vector_field(expd, 1.0, 10000, 1e-7)
###Now compare inversions###
residualJoint=np.array(tf.compose_vector_fields(displacement, inverse)[0])
residualDirect=np.array(tf.compose_vector_fields(displacement, directInverse)[0])
residualExpJoint=np.array(tf.compose_vector_fields(expd, invexpd)[0])
residualExpDirect=np.array(tf.compose_vector_fields(expd, directExpInverse)[0])
rcommon.plotDiffeomorphism(displacement, inverse, residualJoint, 'D-joint')
rcommon.plotDiffeomorphism(expd, invexpd, residualExpJoint, 'expD-joint')
d,invd,res,jacobian=rcommon.plotDiffeomorphism(displacement, directInverse, residualDirect, 'D-direct')
rcommon.plotDiffeomorphism(expd, directExpInverse, residualExpDirect, 'expD-direct')
sp.misc.imsave('circleToC_deformation.png', d)
sp.misc.imsave('circleToC_inverse_deformation.png', invd)
sp.misc.imsave('circleToC_residual_deformation.png', res)
tf.write_double_buffer(np.array(displacement).reshape(-1), '../inverse/experiments/displacement.bin')
tf.write_double_buffer(np.array(displacement).reshape(-1), '../inverse/experiments/displacement_clean.bin')
def runArcesExperiment(rootDir, lambdaParam, maxOuterIter):
#---Load displacement field---
dxName = rootDir + 'Vx.dat'
dyName = rootDir + 'Vy.dat'
dx = np.loadtxt(dxName)
dy = np.loadtxt(dyName)
GT_in = np.ndarray(shape=dx.shape + (2, ), dtype=np.float64)
GT_in[..., 0] = dy
GT_in[..., 1] = dx
GT, GTinv = tf.vector_field_exponential(GT_in)
GTres = tf.compose_vector_fields(GT, GTinv)
#---Load input images---
fnameT1 = rootDir + 't1.jpg'
fnameT2 = rootDir + 't2.jpg'
fnamePD = rootDir + 'pd.jpg'
fnameMask = rootDir + 'Mascara.bmp'
t1 = plt.imread(fnameT1)[..., 0].astype(np.float64)
t2 = plt.imread(fnameT2)[..., 0].astype(np.float64)
pd = plt.imread(fnamePD)[..., 0].astype(np.float64)
t1 = (t1 - t1.min()) / (t1.max() - t1.min())
t2 = (t2 - t2.min()) / (t2.max() - t2.min())
pd = (pd - pd.min()) / (pd.max() - pd.min())
mask = plt.imread(fnameMask).astype(np.float64)
fixed = t1
moving = t2
maskMoving = mask > 0
maskFixed = mask > 0
fixed *= mask
moving *= mask
plt.figure()
plt.subplot(1, 4, 1)
plt.imshow(t1, cmap=plt.cm.gray)
plt.title('Input T1')
plt.subplot(1, 4, 2)
plt.imshow(t2, cmap=plt.cm.gray)
plt.title('Input T2')
plt.subplot(1, 4, 3)
plt.imshow(pd, cmap=plt.cm.gray)
plt.title('Input PD')
plt.subplot(1, 4, 4)
plt.imshow(mask, cmap=plt.cm.gray)
plt.title('Input Mask')
#-------------------------
warpedFixed = rcommon.warpImage(fixed, GT)
print 'Registering T2 (template) to deformed T1 (template)...'
level = 3
movingPyramid = [
img for img in rcommon.pyramid_gaussian_2D(moving, level, maskMoving)
]
fixedPyramid = [
img
for img in rcommon.pyramid_gaussian_2D(warpedFixed, level, maskFixed)
]
plt.figure()
plt.subplot(1, 2, 1)
plt.imshow(moving, cmap=plt.cm.gray)
plt.title('Moving')
plt.subplot(1, 2, 2)
plt.imshow(warpedFixed, cmap=plt.cm.gray)
plt.title('Fixed')
rcommon.plotOverlaidPyramids(movingPyramid, fixedPyramid)
displacementList = []
displacement, inverse = estimateMultimodalDiffeomorphicField2DMultiScale(
movingPyramid, fixedPyramid, lambdaParam, maxOuterIter, 0,
displacementList)
residual = tf.compose_vector_fields(displacement, inverse)
warpPyramid = [
rcommon.warpImage(movingPyramid[i], displacementList[i])
for i in range(level + 1)
]
rcommon.plotOverlaidPyramids(warpPyramid, fixedPyramid)
rcommon.overlayImages(warpPyramid[0], fixedPyramid[0])
displacement[..., 0] *= (maskFixed)
displacement[..., 1] *= (maskFixed)
#----plot deformations---
rcommon.plotDiffeomorphism(GT, GTinv, GTres, 7)
rcommon.plotDiffeomorphism(displacement, inverse, residual, 7)
#----statistics---
nrm = np.sqrt(displacement[..., 0]**2 + displacement[..., 1]**2)
nrm *= maskFixed
maxNorm = np.max(nrm)
residual = ((displacement - GT))**2
meanDisplacementError = np.sqrt(residual.sum(2) * (maskFixed)).mean()
stdevDisplacementError = np.sqrt(residual.sum(2) * (maskFixed)).std()
print 'Max global displacement: ', maxNorm
print 'Mean displacement error: ', meanDisplacementError, '(', stdevDisplacementError, ')'
def runArcesExperiment(rootDir, lambdaParam, maxOuterIter):
#---Load displacement field---
dxName=rootDir+'Vx.dat'
dyName=rootDir+'Vy.dat'
dx=np.loadtxt(dxName)
dy=np.loadtxt(dyName)
GT_in=np.ndarray(shape=dx.shape+(2,), dtype=np.float64)
GT_in[...,0]=dy
GT_in[...,1]=dx
GT, GTinv=tf.vector_field_exponential(GT_in)
GTres=tf.compose_vector_fields(GT, GTinv)
#---Load input images---
fnameT1=rootDir+'t1.jpg'
fnameT2=rootDir+'t2.jpg'
fnamePD=rootDir+'pd.jpg'
fnameMask=rootDir+'Mascara.bmp'
t1=plt.imread(fnameT1)[...,0].astype(np.float64)
t2=plt.imread(fnameT2)[...,0].astype(np.float64)
pd=plt.imread(fnamePD)[...,0].astype(np.float64)
t1=(t1-t1.min())/(t1.max()-t1.min())
t2=(t2-t2.min())/(t2.max()-t2.min())
pd=(pd-pd.min())/(pd.max()-pd.min())
mask=plt.imread(fnameMask).astype(np.float64)
fixed=t1
moving=t2
maskMoving=mask>0
maskFixed=mask>0
fixed*=mask
moving*=mask
plt.figure()
plt.subplot(1,4,1)
plt.imshow(t1, cmap=plt.cm.gray)
plt.title('Input T1')
plt.subplot(1,4,2)
plt.imshow(t2, cmap=plt.cm.gray)
plt.title('Input T2')
plt.subplot(1,4,3)
plt.imshow(pd, cmap=plt.cm.gray)
plt.title('Input PD')
plt.subplot(1,4,4)
plt.imshow(mask, cmap=plt.cm.gray)
plt.title('Input Mask')
#-------------------------
warpedFixed=rcommon.warpImage(fixed,GT)
print 'Registering T2 (template) to deformed T1 (template)...'
level=3
movingPyramid=[img for img in rcommon.pyramid_gaussian_2D(moving, level, maskMoving)]
fixedPyramid=[img for img in rcommon.pyramid_gaussian_2D(warpedFixed, level, maskFixed)]
plt.figure()
plt.subplot(1,2,1)
plt.imshow(moving, cmap=plt.cm.gray)
plt.title('Moving')
plt.subplot(1,2,2)
plt.imshow(warpedFixed, cmap=plt.cm.gray)
plt.title('Fixed')
rcommon.plotOverlaidPyramids(movingPyramid, fixedPyramid)
displacementList=[]
displacement, inverse=estimateMultimodalDiffeomorphicField2DMultiScale(movingPyramid, fixedPyramid, lambdaParam, maxOuterIter, 0, displacementList)
residual=tf.compose_vector_fields(displacement, inverse)
warpPyramid=[rcommon.warpImage(movingPyramid[i], displacementList[i]) for i in range(level+1)]
rcommon.plotOverlaidPyramids(warpPyramid, fixedPyramid)
rcommon.overlayImages(warpPyramid[0], fixedPyramid[0])
displacement[...,0]*=(maskFixed)
displacement[...,1]*=(maskFixed)
#----plot deformations---
rcommon.plotDiffeomorphism(GT, GTinv, GTres, 7)
rcommon.plotDiffeomorphism(displacement, inverse, residual, 7)
#----statistics---
nrm=np.sqrt(displacement[...,0]**2 + displacement[...,1]**2)
nrm*=maskFixed
maxNorm=np.max(nrm)
residual=((displacement-GT))**2
meanDisplacementError=np.sqrt(residual.sum(2)*(maskFixed)).mean()
stdevDisplacementError=np.sqrt(residual.sum(2)*(maskFixed)).std()
print 'Max global displacement: ', maxNorm
print 'Mean displacement error: ', meanDisplacementError,'(',stdevDisplacementError,')'
def test_optimizer_multimodal_2d(lambda_param):
r'''
Registers one of the mid-slices (axial, coronal or sagital) of each input
volume (the volumes are expected to be from diferent modalities and
should already be affine-registered, for example Brainweb t1 vs t2)
'''
fname_moving = 'data/t2/IBSR_t2template_to_01.nii.gz'
fname_fixed = 'data/t1/IBSR_template_to_01.nii.gz'
# fnameMoving = 'data/circle.png'
# fnameFixed = 'data/C.png'
nifti = True
if nifti:
nib_moving = nib.load(fname_moving)
nib_fixed = nib.load(fname_fixed)
moving = nib_moving.get_data().squeeze().astype(np.float64)
fixed = nib_fixed.get_data().squeeze().astype(np.float64)
moving = np.copy(moving, order='C')
fixed = np.copy(fixed, order='C')
shape_moving = moving.shape
shape_fixed = fixed.shape
moving = moving[:, shape_moving[1] // 2, :].copy()
fixed = fixed[:, shape_fixed[1] // 2, :].copy()
moving = (moving - moving.min()) / (moving.max() - moving.min())
fixed = (fixed - fixed.min()) / (fixed.max() - fixed.min())
else:
nib_moving = plt.imread(fname_moving)
nib_fixed = plt.imread(fname_fixed)
moving = nib_moving[:, :, 0].astype(np.float64)
fixed = nib_fixed[:, :, 1].astype(np.float64)
moving = np.copy(moving, order='C')
fixed = np.copy(fixed, order='C')
moving = (moving - moving.min()) / (moving.max() - moving.min())
fixed = (fixed - fixed.min()) / (fixed.max() - fixed.min())
max_iter = [i for i in [25, 50, 100]]
similarity_metric = EMMetric(
2, {
'symmetric': True,
'lambda': lambda_param,
'stepType': SSDMetric.GAUSS_SEIDEL_STEP,
'q_levels': 256,
'max_inner_iter': 20,
'use_double_gradient': True,
'max_step_length': 0.25
})
optimizer_parameters = {
'max_iter': max_iter,
'inversion_iter': 20,
'inversion_tolerance': 1e-3,
'report_status': True
}
update_rule = UpdateRule.Composition()
print('Generating synthetic field...')
#----apply synthetic deformation field to fixed image
ground_truth = rcommon.createDeformationField2D_type2(
fixed.shape[0], fixed.shape[1], 8)
warped_fixed = rcommon.warpImage(fixed, ground_truth)
print('Registering T2 (template) to deformed T1 (template)...')
plt.figure()
rcommon.overlayImages(warped_fixed, moving, False)
registration_optimizer = SymmetricRegistrationOptimizer(
warped_fixed, moving, None, None, similarity_metric, update_rule,
optimizer_parameters)
registration_optimizer.optimize()
#######################show results#################################
displacement = registration_optimizer.get_forward()
direct_inverse = registration_optimizer.get_backward()
moving_to_fixed = np.array(tf.warp_image(moving, displacement))
fixed_to_moving = np.array(tf.warp_image(warped_fixed, direct_inverse))
rcommon.overlayImages(moving_to_fixed, fixed_to_moving, True)
direct_residual, stats = tf.compose_vector_fields(displacement,
direct_inverse)
direct_residual = np.array(direct_residual)
rcommon.plotDiffeomorphism(displacement, direct_inverse, direct_residual,
'inv-direct', 7)
residual = ((displacement - ground_truth))**2
mean_displacement_error = np.sqrt(residual.sum(2) *
(warped_fixed > 0)).mean()
stdev_displacement_error = np.sqrt(residual.sum(2) *
(warped_fixed > 0)).std()
print('Mean displacement error: %0.6f (%0.6f)' %
(mean_displacement_error, stdev_displacement_error))
def test_optimizer_multimodal_2d(lambda_param):
r"""
Registers one of the mid-slices (axial, coronal or sagital) of each input
volume (the volumes are expected to be from diferent modalities and
should already be affine-registered, for example Brainweb t1 vs t2)
"""
fname_moving = "data/t2/IBSR_t2template_to_01.nii.gz"
fname_fixed = "data/t1/IBSR_template_to_01.nii.gz"
# fname_moving = 'data/circle.png'
# fname_fixed = 'data/C.png'
nifti = True
if nifti:
nib_moving = nib.load(fname_moving)
nib_fixed = nib.load(fname_fixed)
moving = nib_moving.get_data().squeeze().astype(np.float64)
fixed = nib_fixed.get_data().squeeze().astype(np.float64)
moving = np.copy(moving, order="C")
fixed = np.copy(fixed, order="C")
moving_shape = moving.shape
fixed_shape = fixed.shape
moving = moving[:, moving_shape[1] // 2, :].copy()
fixed = fixed[:, fixed_shape[1] // 2, :].copy()
# moving = histeq(moving)
# fixed = histeq(fixed)
moving = (moving - moving.min()) / (moving.max() - moving.min())
fixed = (fixed - fixed.min()) / (fixed.max() - fixed.min())
else:
nib_moving = plt.imread(fname_moving)
nib_fixed = plt.imread(fname_fixed)
moving = nib_moving[:, :, 0].astype(np.float64)
fixed = nib_fixed[:, :, 1].astype(np.float64)
moving = np.copy(moving, order="C")
fixed = np.copy(fixed, order="C")
moving = (moving - moving.min()) / (moving.max() - moving.min())
fixed = (fixed - fixed.min()) / (fixed.max() - fixed.min())
# max_iter = [i for i in [25,50,100,100]]
max_iter = [i for i in [25, 50, 100]]
similarity_metric = EMMetric(
{
"symmetric": True,
"lambda": lambda_param,
"step_type": SSDMetric.GAUSS_SEIDEL_STEP,
"q_levels": 256,
"max_inner_iter": 40,
"use_double_gradient": True,
"max_step_length": 0.25,
}
)
optimizer_parameters = {
"max_iter": max_iter,
"inversion_iter": 40,
"inversion_tolerance": 1e-3,
"report_status": True,
}
update_rule = UpdateRule.Composition()
print "Generating synthetic field..."
# ----apply synthetic deformation field to fixed image
ground_truth = rcommon.createDeformationField2D_type2(fixed.shape[0], fixed.shape[1], 8)
warped_fixed = rcommon.warpImage(fixed, ground_truth)
print "Registering T2 (template) to deformed T1 (template)..."
plt.figure()
rcommon.overlayImages(warped_fixed, moving, False)
registration_optimizer = AsymmetricRegistrationOptimizer(
warped_fixed, moving, None, None, similarity_metric, update_rule, optimizer_parameters
)
registration_optimizer.optimize()
#######################show results#################################
displacement = registration_optimizer.get_forward()
direct_inverse = registration_optimizer.get_backward()
moving_to_fixed = np.array(tf.warp_image(moving, displacement))
fixed_to_moving = np.array(tf.warp_image(warped_fixed, direct_inverse))
rcommon.overlayImages(moving_to_fixed, fixed_to_moving, True)
direct_residual, stats = tf.compose_vector_fields(displacement, direct_inverse)
direct_residual = np.array(direct_residual)
rcommon.plotDiffeomorphism(displacement, direct_inverse, direct_residual, "inv-direct", 7)
residual = ((displacement - ground_truth)) ** 2
mean_error = np.sqrt(residual.sum(2) * (warped_fixed > 0)).mean()
stdev_error = np.sqrt(residual.sum(2) * (warped_fixed > 0)).std()
print "Mean displacement error: ", mean_error, "(", stdev_error, ")"
def testCircleToCMonomodalDiffeomorphic(lambdaParam):
import numpy as np
import tensorFieldUtils as tf
import matplotlib.pyplot as plt
import registrationCommon as rcommon
fname0 = 'data/circle.png'
#fname0='data/C_trans.png'
fname1 = 'data/C.png'
circleToCDisplacementName = 'circleToCDisplacement.npy'
circleToCDisplacementInverseName = 'circleToCDisplacementInverse.npy'
nib_moving = plt.imread(fname0)
nib_fixed = plt.imread(fname1)
moving = nib_moving[:, :, 0]
fixed = nib_fixed[:, :, 1]
moving = (moving - moving.min()) / (moving.max() - moving.min())
fixed = (fixed - fixed.min()) / (fixed.max() - fixed.min())
level = 3
maskMoving = moving > 0
maskFixed = fixed > 0
movingPyramid = [
img for img in rcommon.pyramid_gaussian_2D(moving, level,
np.ones_like(maskMoving))
]
fixedPyramid = [
img for img in rcommon.pyramid_gaussian_2D(fixed, level,
np.ones_like(maskFixed))
]
rcommon.plotOverlaidPyramids(movingPyramid, fixedPyramid)
displacementList = []
maxOuterIter = [10, 50, 100, 100, 100, 100, 100, 100, 100]
if (os.path.exists(circleToCDisplacementName)):
displacement = np.load(circleToCDisplacementName)
inverse = np.load(circleToCDisplacementInverseName)
else:
displacement, inverse = estimateMonomodalDiffeomorphicField2DMultiScale(
movingPyramid, fixedPyramid, lambdaParam, maxOuterIter, 0,
displacementList)
np.save(circleToCDisplacementName, displacement)
np.save(circleToCDisplacementInverseName, inverse)
X1, X0 = np.mgrid[0:displacement.shape[0], 0:displacement.shape[1]]
detJacobian = rcommon.computeJacobianField(displacement)
plt.figure()
plt.imshow(detJacobian)
CS = plt.contour(X0, X1, detJacobian, levels=[0.0], colors='b')
plt.clabel(CS, inline=1, fontsize=10)
plt.title('det(J(displacement))')
print 'J range:', '[', detJacobian.min(), detJacobian.max(), ']'
#directInverse=np.array(tf.invert_vector_field(displacement, 0.5, 1000, 1e-7))
directInverse = np.array(
tf.invert_vector_field_fixed_point(displacement, 100, 1e-7))
detJacobianInverse = rcommon.computeJacobianField(directInverse)
plt.figure()
plt.imshow(detJacobianInverse)
CS = plt.contour(X0, X1, detJacobianInverse, levels=[0.0], colors='w')
plt.clabel(CS, inline=1, fontsize=10)
plt.title('det(J(displacement^-1))')
print 'J^-1 range:', '[', detJacobianInverse.min(), detJacobianInverse.max(
), ']'
#directInverse=rcommon.invert_vector_field_fixed_point(displacement, 1000, 1e-7)
residual, stats = tf.compose_vector_fields(displacement, inverse)
residual = np.array(residual)
directResidual, stats = tf.compose_vector_fields(displacement,
directInverse)
directResidual = np.array(directResidual)
# warpPyramid=[rcommon.warpImage(movingPyramid[i], displacementList[i]) for i in range(level+1)]
# rcommon.plotOverlaidPyramids(warpPyramid, fixedPyramid)
# rcommon.overlayImages(warpPyramid[0], fixedPyramid[0])
rcommon.plotDiffeomorphism(displacement, inverse, residual, 'inv-joint', 7)
rcommon.plotDiffeomorphism(displacement, directInverse, directResidual,
'inv-direct', 7)
tf.write_double_buffer(displacement.reshape(-1), 'displacement.bin')
def testInversion(lambdaParam):
fname0 = 'data/circle.png'
fname1 = 'data/C.png'
circleToCDisplacementName = 'circleToCDisplacement.npy'
circleToCDisplacementInverseName = 'circleToCDisplacementInverse.npy'
nib_moving = plt.imread(fname0)
nib_fixed = plt.imread(fname1)
moving = nib_moving[:, :, 0]
fixed = nib_fixed[:, :, 1]
moving = (moving - moving.min()) / (moving.max() - moving.min())
fixed = (fixed - fixed.min()) / (fixed.max() - fixed.min())
level = 3
maskMoving = moving > 0
maskFixed = fixed > 0
movingPyramid = [
img for img in rcommon.pyramid_gaussian_2D(moving, level,
np.ones_like(maskMoving))
]
fixedPyramid = [
img for img in rcommon.pyramid_gaussian_2D(fixed, level,
np.ones_like(maskFixed))
]
rcommon.plotOverlaidPyramids(movingPyramid, fixedPyramid)
displacementList = []
maxOuterIter = [10, 50, 100, 100, 100, 100, 100, 100, 100]
if (os.path.exists(circleToCDisplacementName)):
displacement = np.load(circleToCDisplacementName)
inverse = np.load(circleToCDisplacementInverseName)
else:
displacement, inverse = estimateMonomodalDiffeomorphicField2DMultiScale(
movingPyramid, fixedPyramid, lambdaParam, maxOuterIter, 0,
displacementList)
np.save(circleToCDisplacementName, displacement)
np.save(circleToCDisplacementInverseName, inverse)
print 'vector field exponential'
expd, invexpd = tf.vector_field_exponential(displacement, True)
print 'vector field inversion'
directInverse = tf.invert_vector_field(displacement, 1.0, 10000, 1e-7)
print 'vector field inversion'
directExpInverse = tf.invert_vector_field(expd, 1.0, 10000, 1e-7)
###Now compare inversions###
residualJoint = np.array(
tf.compose_vector_fields(displacement, inverse)[0])
residualDirect = np.array(
tf.compose_vector_fields(displacement, directInverse)[0])
residualExpJoint = np.array(tf.compose_vector_fields(expd, invexpd)[0])
residualExpDirect = np.array(
tf.compose_vector_fields(expd, directExpInverse)[0])
rcommon.plotDiffeomorphism(displacement, inverse, residualJoint, 'D-joint')
rcommon.plotDiffeomorphism(expd, invexpd, residualExpJoint, 'expD-joint')
d, invd, res, jacobian = rcommon.plotDiffeomorphism(
displacement, directInverse, residualDirect, 'D-direct')
rcommon.plotDiffeomorphism(expd, directExpInverse, residualExpDirect,
'expD-direct')
sp.misc.imsave('circleToC_deformation.png', d)
sp.misc.imsave('circleToC_inverse_deformation.png', invd)
sp.misc.imsave('circleToC_residual_deformation.png', res)
tf.write_double_buffer(
np.array(displacement).reshape(-1),
'../inverse/experiments/displacement.bin')
tf.write_double_buffer(
np.array(displacement).reshape(-1),
'../inverse/experiments/displacement_clean.bin')
def test_optimizer_multimodal_2d(lambda_param):
r'''
Registers one of the mid-slices (axial, coronal or sagital) of each input
volume (the volumes are expected to be from diferent modalities and
should already be affine-registered, for example Brainweb t1 vs t2)
'''
fname_moving = 'data/t2/IBSR_t2template_to_01.nii.gz'
fname_fixed = 'data/t1/IBSR_template_to_01.nii.gz'
# fnameMoving = 'data/circle.png'
# fnameFixed = 'data/C.png'
nifti = True
if nifti:
nib_moving = nib.load(fname_moving)
nib_fixed = nib.load(fname_fixed)
moving = nib_moving.get_data().squeeze().astype(np.float64)
fixed = nib_fixed.get_data().squeeze().astype(np.float64)
moving = np.copy(moving, order = 'C')
fixed = np.copy(fixed, order = 'C')
shape_moving = moving.shape
shape_fixed = fixed.shape
moving = moving[:, shape_moving[1]//2, :].copy()
fixed = fixed[:, shape_fixed[1]//2, :].copy()
moving = (moving-moving.min())/(moving.max()-moving.min())
fixed = (fixed-fixed.min())/(fixed.max()-fixed.min())
else:
nib_moving = plt.imread(fname_moving)
nib_fixed = plt.imread(fname_fixed)
moving = nib_moving[:, :, 0].astype(np.float64)
fixed = nib_fixed[:, :, 1].astype(np.float64)
moving = np.copy(moving, order = 'C')
fixed = np.copy(fixed, order = 'C')
moving = (moving-moving.min())/(moving.max() - moving.min())
fixed = (fixed-fixed.min())/(fixed.max() - fixed.min())
max_iter = [i for i in [25, 50, 100]]
similarity_metric = EMMetric(2, {'symmetric':True,
'lambda':lambda_param,
'stepType':SSDMetric.GAUSS_SEIDEL_STEP,
'q_levels':256,
'max_inner_iter':20,
'use_double_gradient':True,
'max_step_length':0.25})
optimizer_parameters = {
'max_iter':max_iter,
'inversion_iter':20,
'inversion_tolerance':1e-3,
'report_status':True}
update_rule = UpdateRule.Composition()
print('Generating synthetic field...')
#----apply synthetic deformation field to fixed image
ground_truth = rcommon.createDeformationField2D_type2(fixed.shape[0],
fixed.shape[1], 8)
warped_fixed = rcommon.warpImage(fixed, ground_truth)
print('Registering T2 (template) to deformed T1 (template)...')
plt.figure()
rcommon.overlayImages(warped_fixed, moving, False)
registration_optimizer = SymmetricRegistrationOptimizer(warped_fixed,
moving,
None, None,
similarity_metric,
update_rule,
optimizer_parameters)
registration_optimizer.optimize()
#######################show results#################################
displacement = registration_optimizer.get_forward()
direct_inverse = registration_optimizer.get_backward()
moving_to_fixed = np.array(tf.warp_image(moving, displacement))
fixed_to_moving = np.array(tf.warp_image(warped_fixed, direct_inverse))
rcommon.overlayImages(moving_to_fixed, fixed_to_moving, True)
direct_residual, stats = tf.compose_vector_fields(displacement,
direct_inverse)
direct_residual = np.array(direct_residual)
rcommon.plotDiffeomorphism(displacement, direct_inverse, direct_residual,
'inv-direct', 7)
residual = ((displacement-ground_truth))**2
mean_displacement_error = np.sqrt(residual.sum(2)*(warped_fixed>0)).mean()
stdev_displacement_error = np.sqrt(residual.sum(2)*(warped_fixed>0)).std()
print('Mean displacement error: %0.6f (%0.6f)'%
(mean_displacement_error, stdev_displacement_error))