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 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 estimateNewMonomodalDiffeomorphicField2D(moving, fixed, lambdaParam,
maxOuterIter,
previousDisplacement,
previousDisplacementInverse):
'''
Warning: in the monomodal case, the parameter lambda must be significantly lower than in the multimodal case. Try lambdaParam=1,
as opposed as lambdaParam=150 used in the multimodal case
'''
innerTolerance = 1e-4
displacement = np.zeros(shape=(moving.shape) + (2, ), dtype=np.float64)
gradientField = np.empty(shape=(moving.shape) + (2, ), dtype=np.float64)
totalDisplacement = np.zeros(shape=(moving.shape) + (2, ),
dtype=np.float64)
totalDisplacementInverse = np.zeros(shape=(moving.shape) + (2, ),
dtype=np.float64)
if (previousDisplacement != None):
totalDisplacement[...] = previousDisplacement
totalDisplacementInverse[...] = previousDisplacementInverse
outerIter = 0
framesToCapture = 5
maxOuterIter = framesToCapture * (
(maxOuterIter + framesToCapture - 1) / framesToCapture)
itersPerCapture = maxOuterIter / framesToCapture
plt.figure()
while (outerIter < maxOuterIter):
outerIter += 1
print 'Outer iter:', outerIter
warped = np.array(tf.warp_image(moving, totalDisplacement, None))
if ((outerIter == 1) or (outerIter % itersPerCapture == 0)):
plt.subplot(1, framesToCapture + 1,
1 + outerIter / itersPerCapture)
rcommon.overlayImages(warped, fixed, False)
plt.title('Iter:' + str(outerIter - 1))
sigmaField = np.ones_like(warped, dtype=np.float64)
deltaField = fixed - warped
gradientField[:, :, 0], gradientField[:, :, 1] = sp.gradient(warped)
maxVariation = 1 + innerTolerance
innerIter = 0
displacement[...] = 0
maxInnerIter = 200
while ((maxVariation > innerTolerance) and (innerIter < maxInnerIter)):
innerIter += 1
maxVariation = tf.iterateDisplacementField2DCYTHON(
deltaField, sigmaField, gradientField, lambdaParam,
displacement, None)
#maxDisplacement=np.max(np.abs(displacement))
expd, invexpd = tf.vector_field_exponential(displacement, True)
totalDisplacement, stats = tf.compose_vector_fields(
displacement, totalDisplacement)
#totalDisplacement=np.array(totalDisplacement)
totalDisplacementInverse, stats = tf.compose_vector_fields(
totalDisplacementInverse, invexpd)
#totalDisplacementInverse=np.array(totalDisplacementInverse)
#if(maxDisplacement<outerTolerance):
#break
print "Iter: ", innerIter, "Max variation:", maxVariation
return totalDisplacement, totalDisplacementInverse
def testEstimateECQMMFMultimodalDeformationField2DMultiScale_synthetic():
##################parameters############
maxGTDisplacement=2
maxPyramidLevel=0
lambdaMeasureField=0.02
lambdaDisplacement=200
mu=0.001
maxOuterIter=20
maxInnerIter=50
tolerance=1e-5
displacementList=[]
#######################################3
#fname0='IBSR_01_to_02.nii.gz'
#fname1='data/t1/IBSR18/IBSR_02/IBSR_02_ana_strip.nii.gz'
fnameMoving='data/t2/t2_icbm_normal_1mm_pn0_rf0_peeled.nii.gz'
fnameFixed='data/t1/t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz'
nib_moving = nib.load(fnameMoving)
nib_fixed = nib.load(fnameFixed)
moving=nib_moving.get_data().squeeze().astype(np.float64)
fixed=nib_fixed.get_data().squeeze().astype(np.float64)
sm=moving.shape
sf=fixed.shape
#---coronal---
moving=moving[:,sm[1]//2,:].copy()
fixed=fixed[:,sf[1]//2,:].copy()
moving=(moving-moving.min())/(moving.max()-moving.min())
fixed=(fixed-fixed.min())/(fixed.max()-fixed.min())
#----apply synthetic deformation field to fixed image
GT=rcommon.createDeformationField_type2(fixed.shape[0], fixed.shape[1], maxGTDisplacement)
fixed=rcommon.warpImage(fixed,GT)
maskMoving=moving>0
maskFixed=fixed>0
movingPyramid=[img for img in rcommon.pyramid_gaussian_2D(moving, maxPyramidLevel, maskMoving)]
fixedPyramid=[img for img in rcommon.pyramid_gaussian_2D(fixed, maxPyramidLevel, 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(fixed, cmap=plt.cm.gray)
plt.title('Fixed')
rcommon.plotOverlaidPyramids(movingPyramid, fixedPyramid)
displacement=estimateECQMMFMultimodalDeformationField2DMultiScale(fixedPyramid, movingPyramid, lambdaMeasureField, lambdaDisplacement, mu, maxOuterIter, maxInnerIter, tolerance, 0,displacementList)
warpedPyramid=[rcommon.warpImage(movingPyramid[i], displacementList[i]) for i in range(maxPyramidLevel+1)]
rcommon.plotOverlaidPyramids(warpedPyramid, fixedPyramid)
rcommon.overlayImages(warpedPyramid[0], fixedPyramid[0])
rcommon.plotDeformationField(displacement)
displacement[...,0]*=(maskMoving + maskFixed)
displacement[...,1]*=(maskMoving + maskFixed)
nrm=np.sqrt(displacement[...,0]**2 + displacement[...,1]**2)
maxNorm=np.max(nrm)
rcommon.plotDeformationField(displacement)
residual=((displacement-GT))**2
meanDisplacementError=np.sqrt(residual.sum(2)*(maskMoving + maskFixed)).mean()
stdevDisplacementError=np.sqrt(residual.sum(2)*(maskMoving + maskFixed)).std()
print 'Max global displacement: ', maxNorm
print 'Mean displacement error: ', meanDisplacementError,'(',stdevDisplacementError,')'
def displayRegistrationResultDiff():
fnameMoving = 'data/affineRegistered/templateT1ToIBSR01T1.nii.gz'
fnameFixed = 'data/t1/IBSR18/IBSR_01/IBSR_01_ana_strip.nii.gz'
nib_fixed = nib.load(fnameFixed)
fixed = nib_fixed.get_data().squeeze()
fixed = np.copy(fixed, order='C')
nib_moving = nib.load(fnameMoving)
moving = nib_moving.get_data().squeeze()
moving = np.copy(moving, order='C')
fnameDisplacement = 'displacement_templateT1ToIBSR01T1_diffMulti.npy'
fnameWarped = 'warped_templateT1ToIBSR01T1_diffMulti.npy'
displacement = np.load(fnameDisplacement)
warped = np.load(fnameWarped)
sh = moving.shape
shown = warped
f = rcommon.overlayImages(shown[:, sh[1] // 4, :], fixed[:, sh[1] // 4, :])
f = rcommon.overlayImages(shown[:, sh[1] // 2, :], fixed[:, sh[1] // 2, :])
f = rcommon.overlayImages(shown[:, 3 * sh[1] // 4, :],
fixed[:, 3 * sh[1] // 4, :])
f = rcommon.overlayImages(shown[sh[0] // 4, :, :], fixed[sh[0] // 4, :, :])
f = rcommon.overlayImages(shown[sh[0] // 2, :, :], fixed[sh[0] // 2, :, :])
f = rcommon.overlayImages(shown[3 * sh[0] // 4, :, :],
fixed[3 * sh[0] // 4, :, :])
f = rcommon.overlayImages(shown[:, :, sh[2] // 4], fixed[:, :, sh[2] // 4])
f = rcommon.overlayImages(shown[:, :, sh[2] // 2], fixed[:, :, sh[2] // 2])
f = rcommon.overlayImages(shown[:, :, 3 * sh[2] // 4],
fixed[:, :, 3 * sh[2] // 4])
del f
del displacement
def displayRegistrationResultDiff():
fnameMoving='data/affineRegistered/templateT1ToIBSR01T1.nii.gz'
fnameFixed='data/t1/IBSR18/IBSR_01/IBSR_01_ana_strip.nii.gz'
nib_fixed = nib.load(fnameFixed)
fixed=nib_fixed.get_data().squeeze()
fixed=np.copy(fixed,order='C')
nib_moving = nib.load(fnameMoving)
moving=nib_moving.get_data().squeeze()
moving=np.copy(moving, order='C')
fnameDisplacement='displacement_templateT1ToIBSR01T1_diffMulti.npy'
fnameWarped='warped_templateT1ToIBSR01T1_diffMulti.npy'
displacement=np.load(fnameDisplacement)
warped=np.load(fnameWarped)
sh=moving.shape
shown=warped
f=rcommon.overlayImages(shown[:,sh[1]//4,:], fixed[:,sh[1]//4,:])
f=rcommon.overlayImages(shown[:,sh[1]//2,:], fixed[:,sh[1]//2,:])
f=rcommon.overlayImages(shown[:,3*sh[1]//4,:], fixed[:,3*sh[1]//4,:])
f=rcommon.overlayImages(shown[sh[0]//4,:,:], fixed[sh[0]//4,:,:])
f=rcommon.overlayImages(shown[sh[0]//2,:,:], fixed[sh[0]//2,:,:])
f=rcommon.overlayImages(shown[3*sh[0]//4,:,:], fixed[3*sh[0]//4,:,:])
f=rcommon.overlayImages(shown[:,:,sh[2]//4], fixed[:,:,sh[2]//4])
f=rcommon.overlayImages(shown[:,:,sh[2]//2], fixed[:,:,sh[2]//2])
f=rcommon.overlayImages(shown[:,:,3*sh[2]//4], fixed[:,:,3*sh[2]//4])
del f
del displacement
def testEstimateMonomodalSyNField2DMultiScale(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()
fixed = nib_fixed.get_data().squeeze()
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 = estimateMonomodalSyNField2DMultiScale(
movingPyramid, fixedPyramid, lambdaParam, maxIter, 0, displacementList)
warpPyramid = [
rcommon.warpImage(movingPyramid[i], displacementList[i])
for i in range(level + 1)
]
rcommon.plotOverlaidPyramids(warpPyramid, fixedPyramid)
rcommon.overlayImages(warpPyramid[0], fixedPyramid[0])
rcommon.plotDeformationField(displacement)
nrm = np.sqrt(displacement[..., 0]**2 + displacement[..., 1]**2)
maxNorm = np.max(nrm)
displacement[..., 0] *= (maskMoving + maskFixed)
displacement[..., 1] *= (maskMoving + maskFixed)
rcommon.plotDeformationField(displacement)
#nrm=np.sqrt(displacement[...,0]**2 + displacement[...,1]**2)
#plt.figure()
#plt.imshow(nrm)
print 'Max global displacement: ', maxNorm
def estimateNewMonomodalDiffeomorphicField2D(moving, fixed, lambdaParam, maxOuterIter, previousDisplacement, previousDisplacementInverse):
'''
Warning: in the monomodal case, the parameter lambda must be significantly lower than in the multimodal case. Try lambdaParam=1,
as opposed as lambdaParam=150 used in the multimodal case
'''
innerTolerance=1e-4
displacement =np.zeros(shape=(moving.shape)+(2,), dtype=np.float64)
gradientField =np.empty(shape=(moving.shape)+(2,), dtype=np.float64)
totalDisplacement=np.zeros(shape=(moving.shape)+(2,), dtype=np.float64)
totalDisplacementInverse=np.zeros(shape=(moving.shape)+(2,), dtype=np.float64)
if(previousDisplacement!=None):
totalDisplacement[...]=previousDisplacement
totalDisplacementInverse[...]=previousDisplacementInverse
outerIter=0
framesToCapture=5
maxOuterIter=framesToCapture*((maxOuterIter+framesToCapture-1)/framesToCapture)
itersPerCapture=maxOuterIter/framesToCapture
plt.figure()
while(outerIter<maxOuterIter):
outerIter+=1
print 'Outer iter:', outerIter
warped=np.array(tf.warp_image(moving, totalDisplacement, None))
if((outerIter==1) or (outerIter%itersPerCapture==0)):
plt.subplot(1,framesToCapture+1, 1+outerIter/itersPerCapture)
rcommon.overlayImages(warped, fixed, False)
plt.title('Iter:'+str(outerIter-1))
sigmaField=np.ones_like(warped, dtype=np.float64)
deltaField=fixed-warped
gradientField[:,:,0], gradientField[:,:,1]=sp.gradient(warped)
maxVariation=1+innerTolerance
innerIter=0
displacement[...]=0
maxInnerIter=200
while((maxVariation>innerTolerance)and(innerIter<maxInnerIter)):
innerIter+=1
maxVariation=tf.iterateDisplacementField2DCYTHON(deltaField, sigmaField, gradientField, lambdaParam, displacement, None)
#maxDisplacement=np.max(np.abs(displacement))
expd, invexpd=tf.vector_field_exponential(displacement, True)
totalDisplacement, stats=tf.compose_vector_fields(displacement, totalDisplacement)
#totalDisplacement=np.array(totalDisplacement)
totalDisplacementInverse, stats=tf.compose_vector_fields(totalDisplacementInverse, invexpd)
#totalDisplacementInverse=np.array(totalDisplacementInverse)
#if(maxDisplacement<outerTolerance):
#break
print "Iter: ",innerIter, "Max variation:",maxVariation
return totalDisplacement, totalDisplacementInverse
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 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 testEstimateMonomodalSyNField2DMultiScale(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()
fixed=nib_fixed.get_data().squeeze()
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=estimateMonomodalSyNField2DMultiScale(movingPyramid, fixedPyramid, lambdaParam, maxIter, 0,displacementList)
warpPyramid=[rcommon.warpImage(movingPyramid[i], displacementList[i]) for i in range(level+1)]
rcommon.plotOverlaidPyramids(warpPyramid, fixedPyramid)
rcommon.overlayImages(warpPyramid[0], fixedPyramid[0])
rcommon.plotDeformationField(displacement)
nrm=np.sqrt(displacement[...,0]**2 + displacement[...,1]**2)
maxNorm=np.max(nrm)
displacement[...,0]*=(maskMoving + maskFixed)
displacement[...,1]*=(maskMoving + maskFixed)
rcommon.plotDeformationField(displacement)
#nrm=np.sqrt(displacement[...,0]**2 + displacement[...,1]**2)
#plt.figure()
#plt.imshow(nrm)
print 'Max global displacement: ', maxNorm
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 testIntersubjectRigidRegistration(fname0, fname1, level, outfname):
nib_left = nib.load(fname0)
nib_right = nib.load(fname1)
left=nib_left.get_data().astype(np.double).squeeze()
right=nib_right.get_data().astype(np.double).squeeze()
leftPyramid=[i for i in rcommon.pyramid_gaussian_3D(left, level)]
rightPyramid=[i for i in rcommon.pyramid_gaussian_3D(right, level)]
plotSlicePyramidsAxial(leftPyramid, rightPyramid)
print 'Estimation started.'
beta=estimateRigidTransformationMultiscale3D(leftPyramid, rightPyramid)
print 'Estimation finished.'
rcommon.applyRigidTransformation3D(left, beta)
sl=np.array(left.shape)//2
sr=np.array(right.shape)//2
rcommon.overlayImages(left[sl[0],:,:], leftPyramid[0][sr[0],:,:])
rcommon.overlayImages(left[sl[0],:,:], right[sr[0],:,:])
affine_transform=AffineTransform('ijk', ['aligned-z=I->S','aligned-y=P->A', 'aligned-x=L->R'], np.eye(4))
left=Image(left, affine_transform)
nipy.save_image(left,outfname)
return beta
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 testIntersubjectRigidRegistration(fname0, fname1, level, outfname):
nib_left = nib.load(fname0)
nib_right = nib.load(fname1)
left = nib_left.get_data().astype(np.double).squeeze()
right = nib_right.get_data().astype(np.double).squeeze()
leftPyramid = [i for i in rcommon.pyramid_gaussian_3D(left, level)]
rightPyramid = [i for i in rcommon.pyramid_gaussian_3D(right, level)]
plotSlicePyramidsAxial(leftPyramid, rightPyramid)
print 'Estimation started.'
beta = estimateRigidTransformationMultiscale3D(leftPyramid, rightPyramid)
print 'Estimation finished.'
rcommon.applyRigidTransformation3D(left, beta)
sl = np.array(left.shape) // 2
sr = np.array(right.shape) // 2
rcommon.overlayImages(left[sl[0], :, :], leftPyramid[0][sr[0], :, :])
rcommon.overlayImages(left[sl[0], :, :], right[sr[0], :, :])
affine_transform = AffineTransform(
'ijk', ['aligned-z=I->S', 'aligned-y=P->A', 'aligned-x=L->R'],
np.eye(4))
left = Image(left, affine_transform)
nipy.save_image(left, outfname)
return beta
def report_status(self):
r'''
Shows the overlaid input images
'''
if self.dim == 2:
plt.figure()
rcommon.overlayImages(self.movingq_means_field,
self.fixedq_means_field, False)
else:
fixed = self.fixed_image
moving = self.moving_image
shape_fixed = fixed.shape
rcommon.overlayImages(moving[:, shape_fixed[1]//2, :],
fixed[:, shape_fixed[1]//2, :])
rcommon.overlayImages(moving[shape_fixed[0]//2, :, :],
fixed[shape_fixed[0]//2, :, :])
rcommon.overlayImages(moving[:, :, shape_fixed[2]//2],
fixed[:, :, shape_fixed[2]//2])
def report_status(self):
r'''
Shows the overlaid input images
'''
if self.dim == 2:
plt.figure()
rcommon.overlayImages(self.movingq_means_field,
self.fixedq_means_field, False)
else:
fixed = self.fixed_image
moving = self.moving_image
shape_fixed = fixed.shape
rcommon.overlayImages(moving[:, shape_fixed[1] // 2, :],
fixed[:, shape_fixed[1] // 2, :])
rcommon.overlayImages(moving[shape_fixed[0] // 2, :, :],
fixed[shape_fixed[0] // 2, :, :])
rcommon.overlayImages(moving[:, :, shape_fixed[2] // 2],
fixed[:, :, shape_fixed[2] // 2])
def estimateNewMonomodalSyNField2D(moving, fixed, fWarp, fInv, mWarp, mInv,
lambdaParam, maxOuterIter):
'''
Warning: in the monomodal case, the parameter lambda must be significantly lower than in the multimodal case. Try lambdaParam=1,
as opposed as lambdaParam=150 used in the multimodal case
'''
innerTolerance = 1e-4
outerTolerance = 1e-3
if (mWarp != None):
totalM = mWarp
totalMInv = mInv
else:
totalM = np.zeros(shape=(fixed.shape) + (2, ), dtype=np.float64)
totalMInv = np.zeros(shape=(fixed.shape) + (2, ), dtype=np.float64)
if (fWarp != None):
totalF = fWarp
totalFInv = fInv
else:
totalF = np.zeros(shape=(moving.shape) + (2, ), dtype=np.float64)
totalFInv = np.zeros(shape=(moving.shape) + (2, ), dtype=np.float64)
outerIter = 0
framesToCapture = 5
maxOuterIter = framesToCapture * (
(maxOuterIter + framesToCapture - 1) / framesToCapture)
itersPerCapture = maxOuterIter / framesToCapture
plt.figure()
while (outerIter < maxOuterIter):
outerIter += 1
print 'Outer iter:', outerIter
wmoving = np.array(tf.warp_image(moving, totalMInv))
wfixed = np.array(tf.warp_image(fixed, totalFInv))
if ((outerIter == 1) or (outerIter % itersPerCapture == 0)):
plt.subplot(1, framesToCapture + 1,
1 + outerIter / itersPerCapture)
rcommon.overlayImages(wmoving, wfixed, False)
plt.title('Iter:' + str(outerIter - 1))
#Compute forward update
sigmaField = np.ones_like(wmoving, dtype=np.float64)
deltaField = wfixed - wmoving
movingGradient = np.empty(shape=(wmoving.shape) + (2, ),
dtype=np.float64)
movingGradient[:, :, 0], movingGradient[:, :, 1] = sp.gradient(wmoving)
maxVariation = 1 + innerTolerance
innerIter = 0
fw = np.zeros(shape=(fixed.shape) + (2, ), dtype=np.float64)
maxInnerIter = 1000
while ((maxVariation > innerTolerance) and (innerIter < maxInnerIter)):
innerIter += 1
maxVariation = tf.iterateDisplacementField2DCYTHON(
deltaField, sigmaField, movingGradient, lambdaParam, fw, None)
#fw*=0.5
totalF, stats = tf.compose_vector_fields(fw, totalF)
totalF = np.array(totalF)
meanDispF = np.mean(np.abs(fw))
#Compute backward field
sigmaField = np.ones_like(wfixed, dtype=np.float64)
deltaField = wmoving - wfixed
fixedGradient = np.empty(shape=(wfixed.shape) + (2, ),
dtype=np.float64)
fixedGradient[:, :, 0], fixedGradient[:, :, 1] = sp.gradient(wfixed)
maxVariation = 1 + innerTolerance
innerIter = 0
mw = np.zeros(shape=(fixed.shape) + (2, ), dtype=np.float64)
maxInnerIter = 1000
while ((maxVariation > innerTolerance) and (innerIter < maxInnerIter)):
innerIter += 1
maxVariation = tf.iterateDisplacementField2DCYTHON(
deltaField, sigmaField, fixedGradient, lambdaParam, mw, None)
#mw*=0.5
totalM, stats = tf.compose_vector_fields(mw, totalM)
totalM = np.array(totalM)
meanDispM = np.mean(np.abs(mw))
totalFInv = np.array(
tf.invert_vector_field_fixed_point(totalF, None, 20, 1e-3, None))
totalMInv = np.array(
tf.invert_vector_field_fixed_point(totalM, None, 20, 1e-3, None))
totalF = np.array(
tf.invert_vector_field_fixed_point(totalFInv, None, 20, 1e-3,
None))
totalM = np.array(
tf.invert_vector_field_fixed_point(totalMInv, None, 20, 1e-3,
None))
# totalFInv=np.array(tf.invert_vector_field(totalF, 0.75, 100, 1e-6))
# totalMInv=np.array(tf.invert_vector_field(totalM, 0.75, 100, 1e-6))
# totalF=np.array(tf.invert_vector_field(totalFInv, 0.75, 100, 1e-6))
# totalM=np.array(tf.invert_vector_field(totalMInv, 0.75, 100, 1e-6))
if (meanDispM + meanDispF < 2 * outerTolerance):
break
print "Iter: ", innerIter, "Mean lateral displacement:", 0.5 * (
meanDispM + meanDispF), "Max variation:", maxVariation
return totalF, totalFInv, totalM, totalMInv
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 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 testEstimateECQMMFMultimodalDeformationField2DMultiScale_synthetic():
##################parameters############
maxGTDisplacement = 2
maxPyramidLevel = 0
lambdaMeasureField = 0.02
lambdaDisplacement = 200
mu = 0.001
maxOuterIter = 20
maxInnerIter = 50
tolerance = 1e-5
displacementList = []
#######################################3
#fname0='IBSR_01_to_02.nii.gz'
#fname1='data/t1/IBSR18/IBSR_02/IBSR_02_ana_strip.nii.gz'
fnameMoving = 'data/t2/t2_icbm_normal_1mm_pn0_rf0_peeled.nii.gz'
fnameFixed = 'data/t1/t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz'
nib_moving = nib.load(fnameMoving)
nib_fixed = nib.load(fnameFixed)
moving = nib_moving.get_data().squeeze().astype(np.float64)
fixed = nib_fixed.get_data().squeeze().astype(np.float64)
sm = moving.shape
sf = fixed.shape
#---coronal---
moving = moving[:, sm[1] // 2, :].copy()
fixed = fixed[:, sf[1] // 2, :].copy()
moving = (moving - moving.min()) / (moving.max() - moving.min())
fixed = (fixed - fixed.min()) / (fixed.max() - fixed.min())
#----apply synthetic deformation field to fixed image
GT = rcommon.createDeformationField_type2(fixed.shape[0], fixed.shape[1],
maxGTDisplacement)
fixed = rcommon.warpImage(fixed, GT)
maskMoving = moving > 0
maskFixed = fixed > 0
movingPyramid = [
img for img in rcommon.pyramid_gaussian_2D(moving, maxPyramidLevel,
maskMoving)
]
fixedPyramid = [
img for img in rcommon.pyramid_gaussian_2D(fixed, maxPyramidLevel,
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(fixed, cmap=plt.cm.gray)
plt.title('Fixed')
rcommon.plotOverlaidPyramids(movingPyramid, fixedPyramid)
displacement = estimateECQMMFMultimodalDeformationField2DMultiScale(
fixedPyramid, movingPyramid, lambdaMeasureField, lambdaDisplacement,
mu, maxOuterIter, maxInnerIter, tolerance, 0, displacementList)
warpedPyramid = [
rcommon.warpImage(movingPyramid[i], displacementList[i])
for i in range(maxPyramidLevel + 1)
]
rcommon.plotOverlaidPyramids(warpedPyramid, fixedPyramid)
rcommon.overlayImages(warpedPyramid[0], fixedPyramid[0])
rcommon.plotDeformationField(displacement)
displacement[..., 0] *= (maskMoving + maskFixed)
displacement[..., 1] *= (maskMoving + maskFixed)
nrm = np.sqrt(displacement[..., 0]**2 + displacement[..., 1]**2)
maxNorm = np.max(nrm)
rcommon.plotDeformationField(displacement)
residual = ((displacement - GT))**2
meanDisplacementError = np.sqrt(
residual.sum(2) * (maskMoving + maskFixed)).mean()
stdevDisplacementError = np.sqrt(
residual.sum(2) * (maskMoving + maskFixed)).std()
print 'Max global displacement: ', maxNorm
print 'Mean displacement error: ', meanDisplacementError, '(', stdevDisplacementError, ')'
def estimateNewMonomodalSyNField2D(moving, fixed, fWarp, fInv, mWarp, mInv, lambdaParam, maxOuterIter):
'''
Warning: in the monomodal case, the parameter lambda must be significantly lower than in the multimodal case. Try lambdaParam=1,
as opposed as lambdaParam=150 used in the multimodal case
'''
innerTolerance=1e-4
outerTolerance=1e-3
if(mWarp!=None):
totalM=mWarp
totalMInv=mInv
else:
totalM=np.zeros(shape=(fixed.shape)+(2,), dtype=np.float64)
totalMInv=np.zeros(shape=(fixed.shape)+(2,), dtype=np.float64)
if(fWarp!=None):
totalF=fWarp
totalFInv=fInv
else:
totalF=np.zeros(shape=(moving.shape)+(2,), dtype=np.float64)
totalFInv=np.zeros(shape=(moving.shape)+(2,), dtype=np.float64)
outerIter=0
framesToCapture=5
maxOuterIter=framesToCapture*((maxOuterIter+framesToCapture-1)/framesToCapture)
itersPerCapture=maxOuterIter/framesToCapture
plt.figure()
while(outerIter<maxOuterIter):
outerIter+=1
print 'Outer iter:', outerIter
wmoving=np.array(tf.warp_image(moving, totalMInv))
wfixed=np.array(tf.warp_image(fixed, totalFInv))
if((outerIter==1) or (outerIter%itersPerCapture==0)):
plt.subplot(1,framesToCapture+1, 1+outerIter/itersPerCapture)
rcommon.overlayImages(wmoving, wfixed, False)
plt.title('Iter:'+str(outerIter-1))
#Compute forward update
sigmaField=np.ones_like(wmoving, dtype=np.float64)
deltaField=wfixed-wmoving
movingGradient =np.empty(shape=(wmoving.shape)+(2,), dtype=np.float64)
movingGradient[:,:,0], movingGradient[:,:,1]=sp.gradient(wmoving)
maxVariation=1+innerTolerance
innerIter=0
fw =np.zeros(shape=(fixed.shape)+(2,), dtype=np.float64)
maxInnerIter=1000
while((maxVariation>innerTolerance)and(innerIter<maxInnerIter)):
innerIter+=1
maxVariation=tf.iterateDisplacementField2DCYTHON(deltaField, sigmaField, movingGradient, lambdaParam, fw, None)
#fw*=0.5
totalF, stats=tf.compose_vector_fields(fw, totalF)
totalF=np.array(totalF);
meanDispF=np.mean(np.abs(fw))
#Compute backward field
sigmaField=np.ones_like(wfixed, dtype=np.float64)
deltaField=wmoving-wfixed
fixedGradient =np.empty(shape=(wfixed.shape)+(2,), dtype=np.float64)
fixedGradient[:,:,0], fixedGradient[:,:,1]=sp.gradient(wfixed)
maxVariation=1+innerTolerance
innerIter=0
mw =np.zeros(shape=(fixed.shape)+(2,), dtype=np.float64)
maxInnerIter=1000
while((maxVariation>innerTolerance)and(innerIter<maxInnerIter)):
innerIter+=1
maxVariation=tf.iterateDisplacementField2DCYTHON(deltaField, sigmaField, fixedGradient, lambdaParam, mw, None)
#mw*=0.5
totalM, stats=tf.compose_vector_fields(mw, totalM)
totalM=np.array(totalM);
meanDispM=np.mean(np.abs(mw))
totalFInv=np.array(tf.invert_vector_field_fixed_point(totalF, None, 20, 1e-3, None))
totalMInv=np.array(tf.invert_vector_field_fixed_point(totalM, None, 20, 1e-3, None))
totalF=np.array(tf.invert_vector_field_fixed_point(totalFInv, None, 20, 1e-3, None))
totalM=np.array(tf.invert_vector_field_fixed_point(totalMInv, None, 20, 1e-3, None))
# totalFInv=np.array(tf.invert_vector_field(totalF, 0.75, 100, 1e-6))
# totalMInv=np.array(tf.invert_vector_field(totalM, 0.75, 100, 1e-6))
# totalF=np.array(tf.invert_vector_field(totalFInv, 0.75, 100, 1e-6))
# totalM=np.array(tf.invert_vector_field(totalMInv, 0.75, 100, 1e-6))
if(meanDispM+meanDispF<2*outerTolerance):
break
print "Iter: ",innerIter, "Mean lateral displacement:", 0.5*(meanDispM+meanDispF), "Max variation:",maxVariation
return totalF, totalFInv, totalM, totalMInv
def report_status(self):
plt.figure()
rcommon.overlayImages(self.moving_image, self.fixed_image, False)
def report_status(self):
plt.figure()
rcommon.overlayImages(self.moving_image, self.fixed_image, False)
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 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"
# 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 showRegistrationResultMidSlices(fnameMoving, fnameFixed, fnameAffine=None):
'''
showRegistrationResultMidSlices('IBSR_01_ana_strip.nii.gz', 't1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', 'IBSR_01_ana_strip_t1_icbm_normal_1mm_pn0_rf0_peeledAffine.txt')
showRegistrationResultMidSlices('warpedDiff_IBSR_01_ana_strip_t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', 't1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_01_ana_strip_IBSR_02_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_02/IBSR_02_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_01_segTRI_ana_IBSR_02_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_02/IBSR_02_segTRI_ana.nii.gz', None)
##Worst pair:
showRegistrationResultMidSlices('warpedDiff_IBSR_16_segTRI_ana_IBSR_12_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_12/IBSR_12_segTRI_ana.nii.gz', None)
showRegistrationResultMidSlices('/opt/registration/data/t1/IBSR18/IBSR_16/IBSR_16_segTRI_ana.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_12/IBSR_12_segTRI_ana.nii.gz', None)
showRegistrationResultMidSlices('/opt/registration/data/t1/IBSR18/IBSR_16/IBSR_16_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_12/IBSR_12_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedAffine_IBSR_16_segTRI_ana_IBSR_12_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_12/IBSR_12_segTRI_ana.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_16_ana_strip_IBSR_12_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_12/IBSR_12_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedAffine_IBSR_16_ana_strip_IBSR_12_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_12/IBSR_12_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('/opt/registration/data/t1/IBSR18/IBSR_10/IBSR_10_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_16/IBSR_16_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedAffine_IBSR_10_ana_strip_IBSR_16_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_16/IBSR_16_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedAffine_IBSR_16_ana_strip_IBSR_10_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_10/IBSR_10_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_16_ana_strip_IBSR_10_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_10/IBSR_10_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_01_ana_strip_IBSR_08_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_08/IBSR_08_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('/opt/registration/data/t1/IBSR18/IBSR_01/IBSR_01_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_08/IBSR_08_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_13_ana_strip_IBSR_10_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_10/IBSR_10_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedAffine_IBSR_13_ana_strip_IBSR_10_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_10/IBSR_10_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('/opt/registration/data/t1/IBSR18/IBSR_13/IBSR_13_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_10/IBSR_10_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_01_ana_strip_IBSR_02_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_10/IBSR_10_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedAffine_IBSR_16_seg_ana_IBSR_10_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_10/IBSR_10_seg_ana.nii.gz', None)
showRegistrationResultMidSlices('/opt/registration/data/t1/IBSR18/IBSR_16/IBSR_16_seg_ana.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_10/IBSR_10_seg_ana.nii.gz', None)
showRegistrationResultMidSlices('/opt/registration/data/t1/IBSR18/IBSR_01/IBSR_01_segTRI_fill_ana.nii.gz', 'warpedAffine_IBSR_10_segTRI_fill_ana_IBSR_01_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_07_ana_strip_IBSR_17_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_17/IBSR_17_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('/opt/registration/data/t1/IBSR18/IBSR_07/IBSR_07_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_17/IBSR_17_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_06_ana_strip_IBSR_17_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_17/IBSR_17_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_07_ana_strip_IBSR_12_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_12/IBSR_12_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_15_ana_strip_IBSR_10_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_10/IBSR_10_ana_strip.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_01_ana_strip_t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', 't1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_01_segTRI_fill_ana_t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', 'data/phantom_1.0mm_normal_crisp.rawb.nii.gz', None)
showRegistrationResultMidSlices('data/t1/t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', 'data/phantom_1.0mm_normal_crisp_peeled.nii.gz', None)
showRegistrationResultMidSlices('data/t2/t2_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', 'data/phantom_1.0mm_normal_crisp_peeled.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_16_ana_strip_t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', 'data/t1/t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', None)
showRegistrationResultMidSlices('warpedAffine_IBSR_16_ana_strip_t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', 'data/t1/t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', None)
showRegistrationResultMidSlices('test16.nii.gz', 'data/t1/t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', None)
showRegistrationResultMidSlices('data/t1/t1_icbm_normal_1mm_pn0_rf0.rawb_peeled.nii.gz', 'data/t1/t1_icbm_normal_1mm_pn0_rf0.rawb_peeled.nii.gz', None)
showRegistrationResultMidSlices('warpedAffine_IBSR_15_ana_strip_t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', 'data/t1/t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_15_ana_strip_t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', 'data/t1/t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', None)
showRegistrationResultMidSlices('warpedAffine_IBSR_01_ana_strip_t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', 'data/t1/t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', None)
showRegistrationResultMidSlices('warpedDiff_IBSR_01_ana_strip_t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', 'data/t1/t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', None)
'''
if(fnameAffine==None):
T=np.eye(4)
else:
T=rcommon.readAntsAffine(fnameAffine)
print 'T:',T
fixed=nib.load(fnameFixed)
F=fixed.get_affine()
print 'F:',F
fixed=fixed.get_data().squeeze().astype(np.float64)
moving=nib.load(fnameMoving)
M=moving.get_affine()
print 'M:',M
moving=moving.get_data().squeeze().astype(np.float64)
initAffine=np.linalg.inv(M).dot(T.dot(F))
fixed=np.copy(fixed, order='C')
moving=np.copy(moving, order='C')
warped=np.array(tf.warp_volume_affine(moving, np.array(fixed.shape).astype(np.int32), initAffine))
sh=warped.shape
rcommon.overlayImages(warped[sh[0]//2,:,:], fixed[sh[0]//2,:,:])
rcommon.overlayImages(warped[:,sh[1]//2,:], fixed[:,sh[1]//2,:])
rcommon.overlayImages(warped[:,:,sh[2]//2], fixed[:,:,sh[2]//2])