def estimateNewMultimodalSyNField3D(moving,
fixed,
fWarp,
fInv,
mWarp,
mInv,
initAffine,
lambdaDisplacement,
quantizationLevels,
maxOuterIter,
reportProgress=False):
'''
fwWarp: forward warp, the displacement field that warps moving towards fixed
bwWarp: backward warp, the displacement field that warps fixed towards moving
initAffine: the affine transformation to bring moving over fixed (this part is not symmetric)
'''
print 'Moving shape:', moving.shape, '. Fixed shape:', fixed.shape
innerTolerance = 1e-3
outerTolerance = 1e-3
fixedMask = (fixed > 0).astype(np.int32)
movingMask = (moving > 0).astype(np.int32)
if (fWarp != None):
totalF = fWarp
totalFInv = fInv
else:
totalF = np.zeros(shape=(fixed.shape) + (3, ), dtype=np.float64)
totalFInv = np.zeros(shape=(fixed.shape) + (3, ), dtype=np.float64)
if (mWarp != None):
totalM = mWarp
totalMInv = mInv
else:
totalM = np.zeros(shape=(moving.shape) + (3, ), dtype=np.float64)
totalMInv = np.zeros(shape=(moving.shape) + (3, ), dtype=np.float64)
finished = False
outerIter = 0
while ((not finished) and (outerIter < maxOuterIter)):
outerIter += 1
if (reportProgress):
print 'Iter:', outerIter, '/', maxOuterIter
#---E step---
wmoving = np.array(tf.warp_volume(moving, totalMInv, initAffine))
wmovingMask = np.array(
tf.warp_discrete_volumeNN(movingMask, totalMInv,
initAffine)).astype(np.int32)
wfixed = np.array(tf.warp_volume(fixed, totalFInv))
wfixedMask = np.array(tf.warp_discrete_volumeNN(
fixedMask, totalFInv)).astype(np.int32)
fixedQ, grayLevels, hist = tf.quantizePositiveVolumeCYTHON(
wfixed, quantizationLevels)
fixedQ = np.array(fixedQ, dtype=np.int32)
movingQ, grayLevels, hist = tf.quantizePositiveVolumeCYTHON(
wmoving, quantizationLevels)
movingQ = np.array(movingQ, dtype=np.int32)
trust = wfixedMask * wmovingMask
meansMoving, variancesMoving = tf.computeMaskedVolumeClassStatsCYTHON(
trust, wmoving, quantizationLevels, fixedQ)
meansFixed, variancesFixed = tf.computeMaskedVolumeClassStatsCYTHON(
trust, wfixed, quantizationLevels, movingQ)
meansMoving[0] = 0
meansFixed[0] = 0
meansMoving = np.array(meansMoving)
meansFixed = np.array(meansFixed)
variancesMoving = np.array(variancesMoving)
sigmaFieldMoving = variancesMoving[fixedQ]
variancesFixed = np.array(variancesFixed)
sigmaFieldFixed = variancesFixed[movingQ]
deltaFieldMoving = meansMoving[fixedQ] - wmoving
deltaFieldFixed = meansFixed[movingQ] - wfixed
#--M step--
movingGradient = np.empty(shape=(moving.shape) + (3, ),
dtype=np.float64)
movingGradient[:, :, :,
0], movingGradient[:, :, :,
1], movingGradient[:, :, :,
2] = sp.gradient(
wmoving)
#iterate forward field
maxVariation = 1 + innerTolerance
innerIter = 0
maxInnerIter = 100
fw = np.zeros_like(totalF)
while ((maxVariation > innerTolerance) and (innerIter < maxInnerIter)):
innerIter += 1
maxVariation = tf.iterateDisplacementField3DCYTHON(
deltaFieldMoving, sigmaFieldMoving, movingGradient,
lambdaDisplacement, totalF, fw, None)
del movingGradient
fw *= 0.5
totalF = np.array(tf.compose_vector_fields3D(
fw, totalF)) #Multiply fw by 0.5??
nrm = np.sqrt(fw[..., 0]**2 + fw[..., 1]**2 + fw[..., 2]**2)
del fw
#iterate backward field
fixedGradient = np.empty(shape=(fixed.shape) + (3, ), dtype=np.float64)
fixedGradient[:, :, :,
0], fixedGradient[:, :, :,
1], fixedGradient[:, :, :,
2] = sp.gradient(
wfixed)
maxVariation = 1 + innerTolerance
innerIter = 0
maxInnerIter = 100
mw = np.zeros_like(totalM)
while ((maxVariation > innerTolerance) and (innerIter < maxInnerIter)):
innerIter += 1
maxVariation = tf.iterateDisplacementField3DCYTHON(
deltaFieldFixed, sigmaFieldFixed, fixedGradient,
lambdaDisplacement, totalM, mw, None)
del fixedGradient
mw *= 0.5
totalM = np.array(tf.compose_vector_fields3D(
mw, totalM)) #Multiply bw by 0.5??
nrm = np.sqrt(mw[..., 0]**2 + mw[..., 1]**2 + mw[..., 2]**2)
del mw
#invert fields
totalFInv = np.array(
tf.invert_vector_field_fixed_point3D(totalF, 20, 1e-6))
totalMInv = np.array(
tf.invert_vector_field_fixed_point3D(totalM, 20, 1e-6))
totalF = np.array(
tf.invert_vector_field_fixed_point3D(totalFInv, 20, 1e-6))
totalM = np.array(
tf.invert_vector_field_fixed_point3D(totalMInv, 20, 1e-6))
maxDisplacement = np.mean(nrm)
if ((maxDisplacement < outerTolerance) or (outerIter >= maxOuterIter)):
finished = True
print "Iter: ", outerIter, "Mean displacement:", maxDisplacement, "Max variation:", maxVariation
return totalF, totalFInv, totalM, totalMInv
def estimateNewMultimodalDiffeomorphicField3D(moving,
fixed,
initAffine,
lambdaDisplacement,
quantizationLevels,
maxOuterIter,
previousDisplacement,
reportProgress=False):
innerTolerance = 1e-3
outerTolerance = 1e-3
displacement = np.empty(shape=(fixed.shape) + (3, ), dtype=np.float64)
residuals = np.zeros(shape=(fixed.shape), dtype=np.float64)
gradientField = np.empty(shape=(fixed.shape) + (3, ), dtype=np.float64)
totalDisplacement = np.zeros(shape=(fixed.shape) + (3, ), dtype=np.float64)
if (previousDisplacement != None):
totalDisplacement[...] = previousDisplacement
fixedQ = None
grayLevels = None
fixedQ, grayLevels, hist = tf.quantizePositiveVolumeCYTHON(
fixed, quantizationLevels)
fixedQ = np.array(fixedQ, dtype=np.int32)
finished = False
outerIter = 0
maxDisplacement = None
maxVariation = None
maxResidual = 0
fixedMask = (fixed > 0).astype(np.int32)
movingMask = (moving > 0).astype(np.int32)
trustRegion = fixedMask * np.array(
tf.warp_discrete_volumeNNAffine(
movingMask, np.array(fixedMask.shape, dtype=np.int32),
initAffine)) #consider only the overlap after affine registration
while ((not finished) and (outerIter < maxOuterIter)):
outerIter += 1
if (reportProgress):
print 'Iter:', outerIter, '/', maxOuterIter
#sys.stdout.flush()
#---E step---
#print "Warping..."
#sys.stdout.flush()
warped = np.array(tf.warp_volume(moving, totalDisplacement,
initAffine))
warpedMask = np.array(
tf.warp_discrete_volumeNN(
trustRegion, totalDisplacement, np.eye(4))).astype(
np.int32) #the affine mapping was already applied
#print "Warping NN..."
#sys.stdout.flush()
#warpedMovingMask=np.array(tf.warp_volumeNN(movingMask, totalDisplacement)).astype(np.int32)
#print "Class stats..."
#sys.stdout.flush()
means, variances = tf.computeMaskedVolumeClassStatsCYTHON(
warpedMask, warped, quantizationLevels, fixedQ)
means[0] = 0
means = np.array(means)
variances = np.array(variances)
sigmaField = variances[fixedQ]
deltaField = means[
fixedQ] - warped #########Delta-field using Arce's rule
#--M step--
g0, g1, g2 = sp.gradient(warped)
gradientField[:, :, :, 0] = g0
gradientField[:, :, :, 1] = g1
gradientField[:, :, :, 2] = g2
maxVariation = 1 + innerTolerance
innerIter = 0
maxInnerIter = 100
displacement[...] = 0
#print "Iterating..."
#sys.stdout.flush()
while ((maxVariation > innerTolerance) and (innerIter < maxInnerIter)):
innerIter += 1
maxVariation = tf.iterateDisplacementField3DCYTHON(
deltaField, sigmaField, gradientField, lambdaDisplacement,
totalDisplacement, displacement, residuals)
opt = np.max(residuals)
if (maxResidual < opt):
maxResidual = opt
#--accumulate displacement--
#print "Exponential3D. Range D:", displacement.min(), displacement.max()
#sys.stdout.flush()
expd, inverseNone = tf.vector_field_exponential3D(displacement, False)
expd = np.array(expd)
#print "Range expd:", expd.min(), expd.max(), "Range TD:", totalDisplacement.min(), totalDisplacement.max()
#print "Compose vector fields..."
#sys.stdout.flush()
totalDisplacement, stats = tf.compose_vector_fields3D(
expd, totalDisplacement)
totalDisplacement = np.array(totalDisplacement)
#print "Composed rage:", totalDisplacement.min(), totalDisplacement.max()
#sys.stdout.flush()
#--check stop condition--
nrm = np.sqrt(displacement[..., 0]**2 + displacement[..., 1]**2 +
displacement[..., 2]**2)
#maxDisplacement=np.max(nrm)
maxDisplacement = np.mean(nrm)
if ((maxDisplacement < outerTolerance) or (outerIter >= maxOuterIter)):
finished = True
print "Iter: ", outerIter, "Mean displacement:", maxDisplacement, "Max variation:", maxVariation, "Max residual:", maxResidual
#sh=fixed.shape
#rcommon.overlayImages(warped[:,sh[1]//2,:], fixed[:,sh[1]//2,:])
#rcommon.overlayImages(warped[:,sh[1]//2,:]*warpedMask[:,sh[1]//2,:], fixed[:,sh[1]//2,:])
#sys.stdout.flush()
if (previousDisplacement != None):
#print 'Range TD:', totalDisplacement.min(), totalDisplacement.max(),'. Range PD:', previousDisplacement.min(), previousDisplacement.max()
#sys.stdout.flush()
return totalDisplacement - previousDisplacement
return totalDisplacement
def estimateNewMultimodalSyNField3D(moving, fixed, fWarp, fInv, mWarp, mInv, initAffine, lambdaDisplacement, quantizationLevels, maxOuterIter, reportProgress=False):
'''
fwWarp: forward warp, the displacement field that warps moving towards fixed
bwWarp: backward warp, the displacement field that warps fixed towards moving
initAffine: the affine transformation to bring moving over fixed (this part is not symmetric)
'''
print 'Moving shape:',moving.shape,'. Fixed shape:',fixed.shape
innerTolerance=1e-3
outerTolerance=1e-3
fixedMask=(fixed>0).astype(np.int32)
movingMask=(moving>0).astype(np.int32)
if(fWarp!=None):
totalF=fWarp
totalFInv=fInv
else:
totalF =np.zeros(shape=(fixed.shape)+(3,), dtype=np.float64)
totalFInv =np.zeros(shape=(fixed.shape)+(3,), dtype=np.float64)
if(mWarp!=None):
totalM=mWarp
totalMInv=mInv
else:
totalM =np.zeros(shape=(moving.shape)+(3,), dtype=np.float64)
totalMInv=np.zeros(shape=(moving.shape)+(3,), dtype=np.float64)
finished=False
outerIter=0
while((not finished) and (outerIter<maxOuterIter)):
outerIter+=1
if(reportProgress):
print 'Iter:',outerIter,'/',maxOuterIter
#---E step---
wmoving=np.array(tf.warp_volume(moving, totalMInv, initAffine))
wmovingMask=np.array(tf.warp_discrete_volumeNN(movingMask, totalMInv, initAffine)).astype(np.int32)
wfixed=np.array(tf.warp_volume(fixed, totalFInv))
wfixedMask=np.array(tf.warp_discrete_volumeNN(fixedMask, totalFInv)).astype(np.int32)
fixedQ, grayLevels, hist=tf.quantizePositiveVolumeCYTHON(wfixed, quantizationLevels)
fixedQ=np.array(fixedQ, dtype=np.int32)
movingQ, grayLevels, hist=tf.quantizePositiveVolumeCYTHON(wmoving, quantizationLevels)
movingQ=np.array(movingQ, dtype=np.int32)
trust=wfixedMask*wmovingMask
meansMoving, variancesMoving=tf.computeMaskedVolumeClassStatsCYTHON(trust, wmoving, quantizationLevels, fixedQ)
meansFixed, variancesFixed=tf.computeMaskedVolumeClassStatsCYTHON(trust, wfixed, quantizationLevels, movingQ)
meansMoving[0]=0
meansFixed[0]=0
meansMoving=np.array(meansMoving)
meansFixed=np.array(meansFixed)
variancesMoving=np.array(variancesMoving)
sigmaFieldMoving=variancesMoving[fixedQ]
variancesFixed=np.array(variancesFixed)
sigmaFieldFixed=variancesFixed[movingQ]
deltaFieldMoving=meansMoving[fixedQ]-wmoving
deltaFieldFixed=meansFixed[movingQ]-wfixed
#--M step--
movingGradient =np.empty(shape=(moving.shape)+(3,), dtype=np.float64)
movingGradient[:,:,:,0], movingGradient[:,:,:,1], movingGradient[:,:,:,2]=sp.gradient(wmoving)
#iterate forward field
maxVariation=1+innerTolerance
innerIter=0
maxInnerIter=100
fw=np.zeros_like(totalF)
while((maxVariation>innerTolerance)and(innerIter<maxInnerIter)):
innerIter+=1
maxVariation=tf.iterateDisplacementField3DCYTHON(deltaFieldMoving, sigmaFieldMoving, movingGradient, lambdaDisplacement, totalF, fw, None)
del movingGradient
fw*=0.5
totalF=np.array(tf.compose_vector_fields3D(fw, totalF))#Multiply fw by 0.5??
nrm=np.sqrt(fw[...,0]**2+fw[...,1]**2+fw[...,2]**2)
del fw
#iterate backward field
fixedGradient =np.empty(shape=(fixed.shape)+(3,), dtype=np.float64)
fixedGradient[:,:,:,0], fixedGradient[:,:,:,1], fixedGradient[:,:,:,2]=sp.gradient(wfixed)
maxVariation=1+innerTolerance
innerIter=0
maxInnerIter=100
mw=np.zeros_like(totalM)
while((maxVariation>innerTolerance)and(innerIter<maxInnerIter)):
innerIter+=1
maxVariation=tf.iterateDisplacementField3DCYTHON(deltaFieldFixed, sigmaFieldFixed, fixedGradient, lambdaDisplacement, totalM, mw, None)
del fixedGradient
mw*=0.5
totalM=np.array(tf.compose_vector_fields3D(mw, totalM))#Multiply bw by 0.5??
nrm=np.sqrt(mw[...,0]**2+mw[...,1]**2+mw[...,2]**2)
del mw
#invert fields
totalFInv=np.array(tf.invert_vector_field_fixed_point3D(totalF, 20, 1e-6))
totalMInv=np.array(tf.invert_vector_field_fixed_point3D(totalM, 20, 1e-6))
totalF=np.array(tf.invert_vector_field_fixed_point3D(totalFInv, 20, 1e-6))
totalM=np.array(tf.invert_vector_field_fixed_point3D(totalMInv, 20, 1e-6))
maxDisplacement=np.mean(nrm)
if((maxDisplacement<outerTolerance)or(outerIter>=maxOuterIter)):
finished=True
print "Iter: ",outerIter, "Mean displacement:", maxDisplacement, "Max variation:",maxVariation
return totalF, totalFInv, totalM, totalMInv
def estimateNewMultimodalDiffeomorphicField3D(moving, fixed, initAffine, lambdaDisplacement, quantizationLevels, maxOuterIter, previousDisplacement, reportProgress=False):
innerTolerance=1e-3
outerTolerance=1e-3
displacement =np.empty(shape=(fixed.shape)+(3,), dtype=np.float64)
residuals=np.zeros(shape=(fixed.shape), dtype=np.float64)
gradientField =np.empty(shape=(fixed.shape)+(3,), dtype=np.float64)
totalDisplacement=np.zeros(shape=(fixed.shape)+(3,), dtype=np.float64)
if(previousDisplacement!=None):
totalDisplacement[...]=previousDisplacement
fixedQ=None
grayLevels=None
fixedQ, grayLevels, hist=tf.quantizePositiveVolumeCYTHON(fixed, quantizationLevels)
fixedQ=np.array(fixedQ, dtype=np.int32)
finished=False
outerIter=0
maxDisplacement=None
maxVariation=None
maxResidual=0
fixedMask=(fixed>0).astype(np.int32)
movingMask=(moving>0).astype(np.int32)
trustRegion=fixedMask*np.array(tf.warp_discrete_volumeNNAffine(movingMask, np.array(fixedMask.shape, dtype=np.int32), initAffine))#consider only the overlap after affine registration
while((not finished) and (outerIter<maxOuterIter)):
outerIter+=1
if(reportProgress):
print 'Iter:',outerIter,'/',maxOuterIter
#sys.stdout.flush()
#---E step---
#print "Warping..."
#sys.stdout.flush()
warped=np.array(tf.warp_volume(moving, totalDisplacement, initAffine))
warpedMask=np.array(tf.warp_discrete_volumeNN(trustRegion, totalDisplacement, np.eye(4))).astype(np.int32)#the affine mapping was already applied
#print "Warping NN..."
#sys.stdout.flush()
#warpedMovingMask=np.array(tf.warp_volumeNN(movingMask, totalDisplacement)).astype(np.int32)
#print "Class stats..."
#sys.stdout.flush()
means, variances=tf.computeMaskedVolumeClassStatsCYTHON(warpedMask, warped, quantizationLevels, fixedQ)
means[0]=0
means=np.array(means)
variances=np.array(variances)
sigmaField=variances[fixedQ]
deltaField=means[fixedQ]-warped#########Delta-field using Arce's rule
#--M step--
g0, g1, g2=sp.gradient(warped)
gradientField[:,:,:,0]=g0
gradientField[:,:,:,1]=g1
gradientField[:,:,:,2]=g2
maxVariation=1+innerTolerance
innerIter=0
maxInnerIter=100
displacement[...]=0
#print "Iterating..."
#sys.stdout.flush()
while((maxVariation>innerTolerance)and(innerIter<maxInnerIter)):
innerIter+=1
maxVariation=tf.iterateDisplacementField3DCYTHON(deltaField, sigmaField, gradientField, lambdaDisplacement, totalDisplacement, displacement, residuals)
opt=np.max(residuals)
if(maxResidual<opt):
maxResidual=opt
#--accumulate displacement--
#print "Exponential3D. Range D:", displacement.min(), displacement.max()
#sys.stdout.flush()
expd, inverseNone=tf.vector_field_exponential3D(displacement, False)
expd=np.array(expd)
#print "Range expd:", expd.min(), expd.max(), "Range TD:", totalDisplacement.min(), totalDisplacement.max()
#print "Compose vector fields..."
#sys.stdout.flush()
totalDisplacement, stats=tf.compose_vector_fields3D(expd, totalDisplacement)
totalDisplacement=np.array(totalDisplacement)
#print "Composed rage:", totalDisplacement.min(), totalDisplacement.max()
#sys.stdout.flush()
#--check stop condition--
nrm=np.sqrt(displacement[...,0]**2+displacement[...,1]**2+displacement[...,2]**2)
#maxDisplacement=np.max(nrm)
maxDisplacement=np.mean(nrm)
if((maxDisplacement<outerTolerance)or(outerIter>=maxOuterIter)):
finished=True
print "Iter: ",outerIter, "Mean displacement:", maxDisplacement, "Max variation:",maxVariation, "Max residual:", maxResidual
#sh=fixed.shape
#rcommon.overlayImages(warped[:,sh[1]//2,:], fixed[:,sh[1]//2,:])
#rcommon.overlayImages(warped[:,sh[1]//2,:]*warpedMask[:,sh[1]//2,:], fixed[:,sh[1]//2,:])
#sys.stdout.flush()
if(previousDisplacement!=None):
#print 'Range TD:', totalDisplacement.min(), totalDisplacement.max(),'. Range PD:', previousDisplacement.min(), previousDisplacement.max()
#sys.stdout.flush()
return totalDisplacement-previousDisplacement
return totalDisplacement
