def iterativeBackPropagation(hrImage, lrImages, lrMasks, transforms, H,
itermax):
y = []
for i in range(len(lrImages)):
y.append(
convert_image_to_vector(lrImages[i]) *
convert_image_to_vector(lrMasks[i]))
x = convert_image_to_vector(hrImage)
outputImage = nibabel.Nifti1Image(hrImage.get_data(), hrImage.affine)
hrMaskSum = np.zeros(hrImage.get_data().shape, dtype=np.float32)
for i in range(len(lrImages)):
tmp1 = apply_affine_itk_transform_on_image(input_image=lrMasks[i],
transform=transforms[i][0],
center=transforms[i][1],
reference_image=hrImage,
order=0)
hrMaskSum += tmp1.get_data()
index = np.nonzero(hrMaskSum)
for j in range(itermax):
#simulation and error computation
hrError = np.zeros(hrImage.get_data().shape, dtype=np.float32)
for i in range(len(lrImages)):
lrError = convert_vector_to_image(H[i].dot(x) - y[i], lrImages[i])
tmp2 = apply_affine_itk_transform_on_image(
input_image=lrError,
transform=transforms[i][0],
center=transforms[i][1],
reference_image=hrImage,
order=1)
hrError += tmp2.get_data()
hrError2 = np.zeros(hrImage.get_data().shape, dtype=np.float32)
hrError2[index] = hrError[index] / hrMaskSum[index]
#update hr image and x
outputImage = nibabel.Nifti1Image(outputImage.get_data() - hrError2,
hrImage.affine)
nibabel.save(outputImage, 'ibp_iter' + str(j) + '.nii.gz')
x = convert_image_to_vector(outputImage)
return outputImage
def iterativeBackPropagation(hrImage, lrImages, lrMasks, transforms, H,
itermax, interpOrder):
#Convert LR images to a list of vectors
y = []
for i in range(len(lrImages)):
y.append(
convert_image_to_vector(lrImages[i]) *
convert_image_to_vector(lrMasks[i]))
#Convert HR Image to vector
x = convert_image_to_vector(hrImage)
outputImage = nibabel.Nifti1Image(hrImage.get_data(), hrImage.affine)
#Compute HR mask
hrMaskSum = np.zeros(hrImage.get_data().shape, dtype=np.float32)
for i in range(len(lrImages)):
tmp1 = apply_affine_itk_transform_on_image(input_image=lrMasks[i],
transform=transforms[i][0],
center=transforms[i][1],
reference_image=hrImage,
order=0)
hrMaskSum += tmp1.get_data()
#index = np.nonzero(hrMaskSum)
for j in range(itermax):
error = ibpComputeError(x, H, y,
nibabel.Nifti1Image(hrMaskSum, hrImage.affine),
lrImages, transforms, interpOrder)
# #simulation and error computation
# hrError = np.zeros(hrImage.get_data().shape, dtype=np.float32)
#
# for i in range(len(lrImages)):
# lrError = convert_vector_to_image(H[i].dot(x)-y[i], lrImages[i])
# tmp2 = apply_affine_itk_transform_on_image(input_image = lrError, transform=transforms[i][0], center=transforms[i][1], reference_image=hrImage, order=interpOrder)
# hrError += tmp2.get_data()
#
# hrError2 = np.zeros(hrImage.get_data().shape, dtype=np.float32)
# hrError2[index] = hrError[index] / hrMaskSum[index]
#
#filter error map
from skimage.restoration import denoise_tv_chambolle
hrError2 = denoise_tv_chambolle(error, weight=5)
#update hr image and x
outputImage = nibabel.Nifti1Image(outputImage.get_data() - hrError2,
hrImage.affine)
nibabel.save(nibabel.Nifti1Image(hrError2, hrImage.affine),
'error_iter' + str(j) + '.nii.gz')
nibabel.save(outputImage, 'ibp_iter' + str(j) + '.nii.gz')
x = convert_image_to_vector(outputImage)
return outputImage
def iterativeBackPropagation(hrImage, lrImages, lrMasks, transforms, H, itermax, interpOrder):
# Convert LR images to a list of vectors
y = []
for i in range(len(lrImages)):
y.append(convert_image_to_vector(lrImages[i]) * convert_image_to_vector(lrMasks[i]))
# Convert HR Image to vector
x = convert_image_to_vector(hrImage)
outputImage = nibabel.Nifti1Image(hrImage.get_data(), hrImage.affine)
# Compute HR mask
hrMaskSum = np.zeros(hrImage.get_data().shape, dtype=np.float32)
for i in range(len(lrImages)):
tmp1 = apply_affine_itk_transform_on_image(
input_image=lrMasks[i],
transform=transforms[i][0],
center=transforms[i][1],
reference_image=hrImage,
order=0,
)
hrMaskSum += tmp1.get_data()
# index = np.nonzero(hrMaskSum)
for j in range(itermax):
error = ibpComputeError(
x, H, y, nibabel.Nifti1Image(hrMaskSum, hrImage.affine), lrImages, transforms, interpOrder
)
# #simulation and error computation
# hrError = np.zeros(hrImage.get_data().shape, dtype=np.float32)
#
# for i in range(len(lrImages)):
# lrError = convert_vector_to_image(H[i].dot(x)-y[i], lrImages[i])
# tmp2 = apply_affine_itk_transform_on_image(input_image = lrError, transform=transforms[i][0], center=transforms[i][1], reference_image=hrImage, order=interpOrder)
# hrError += tmp2.get_data()
#
# hrError2 = np.zeros(hrImage.get_data().shape, dtype=np.float32)
# hrError2[index] = hrError[index] / hrMaskSum[index]
#
# filter error map
from skimage.restoration import denoise_tv_chambolle
hrError2 = denoise_tv_chambolle(error, weight=5)
# update hr image and x
outputImage = nibabel.Nifti1Image(outputImage.get_data() - hrError2, hrImage.affine)
nibabel.save(nibabel.Nifti1Image(hrError2, hrImage.affine), "error_iter" + str(j) + ".nii.gz")
nibabel.save(outputImage, "ibp_iter" + str(j) + ".nii.gz")
x = convert_image_to_vector(outputImage)
return outputImage
def ibpComputeError(x, H, y, hrMaskSum, lrImages, transforms, interpOrder):
hrError = np.zeros(hrMaskSum.get_data().shape, dtype=np.float32)
#Loop over LR image to compute the sum of errors
for i in range(len(lrImages)):
lrError = convert_vector_to_image(H[i].dot(x) - y[i], lrImages[i])
tmp2 = apply_affine_itk_transform_on_image(input_image=lrError,
transform=transforms[i][0],
center=transforms[i][1],
reference_image=hrMaskSum,
order=interpOrder)
hrError += tmp2.get_data()
#Normalize the error image
hrErrorNorm = np.zeros(hrMaskSum.get_data().shape, dtype=np.float32)
index = np.nonzero(hrMaskSum.get_data())
hrErrorNorm[index] = hrError[index] / hrMaskSum.get_data()[index]
return hrErrorNorm
def ibpComputeError(x, H, y, hrMaskSum, lrImages, transforms, interpOrder):
hrError = np.zeros(hrMaskSum.get_data().shape, dtype=np.float32)
# Loop over LR image to compute the sum of errors
for i in range(len(lrImages)):
lrError = convert_vector_to_image(H[i].dot(x) - y[i], lrImages[i])
tmp2 = apply_affine_itk_transform_on_image(
input_image=lrError,
transform=transforms[i][0],
center=transforms[i][1],
reference_image=hrMaskSum,
order=interpOrder,
)
hrError += tmp2.get_data()
# Normalize the error image
hrErrorNorm = np.zeros(hrMaskSum.get_data().shape, dtype=np.float32)
index = np.nonzero(hrMaskSum.get_data())
hrErrorNorm[index] = hrError[index] / hrMaskSum.get_data()[index]
return hrErrorNorm
#Intensity correction To do
#N4 on initHR
#local correction
#New init HR
if args.bias == True:
initHRImage_N4 = apply_N4_on_image(initHRImage, shrink_factor=1)
xN4 = convert_image_to_vector(initHRImage_N4)
hrN4Data = np.zeros(initHRImage.get_data().shape)
for i in range(len(inputImages)):
simu = convert_vector_to_image(HList[i].dot(xN4), inputImages[i])
im = gaussian_biais_correction(inputImages[i], simu, 5)
warped = apply_affine_itk_transform_on_image(
input_image=im,
transform=inputTransforms[i][0],
center=inputTransforms[i][1],
reference_image=initHRImage,
order=3)
hrN4Data += (warped.get_data() / np.float32(len(inputImages)))
initHRImage = nibabel.Nifti1Image(hrN4Data, initHRImage.affine)
#Compute x
x = convert_image_to_vector(initHRImage)
maskX = convert_image_to_vector(maskHRImage)
#Let mask the HR image
x = x * maskX
#loop over LR images and stack y and masks
maskList = []
yList = []
for i in range(len(inputImages)):
# N4 on initHR
# local correction
# New init HR
if args.bias == True:
initHRImage_N4 = apply_N4_on_image(initHRImage, shrink_factor=1)
xN4 = convert_image_to_vector(initHRImage_N4)
hrN4Data = np.zeros(initHRImage.get_data().shape)
for i in range(len(inputImages)):
simu = convert_vector_to_image(HList[i].dot(xN4), inputImages[i])
im = gaussian_biais_correction(inputImages[i], simu, 5)
warped = apply_affine_itk_transform_on_image(
input_image=im,
transform=inputTransforms[i][0],
center=inputTransforms[i][1],
reference_image=initHRImage,
order=3,
)
hrN4Data += warped.get_data() / np.float32(len(inputImages))
initHRImage = nibabel.Nifti1Image(hrN4Data, initHRImage.affine)
# Compute x
x = convert_image_to_vector(initHRImage)
maskX = convert_image_to_vector(maskHRImage)
# Let mask the HR image
x = x * maskX
# loop over LR images and stack y and masks
maskList = []
yList = []
help='Moving image filename (required)',
type=str,
required=True)
parser.add_argument('-t',
'--transform',
help='Transform for each input image (required)',
type=str,
required=True)
parser.add_argument('-r',
'--ref',
help='Reference image (required)',
type=str,
required=True)
parser.add_argument('-o',
'--output',
help='Deformed image filename (required)',
type=str,
required=True)
args = parser.parse_args()
movImage = nibabel.load(args.input)
refImage = nibabel.load(args.ref)
transform = read_itk_transform(args.transform)
outputImage = apply_affine_itk_transform_on_image(input_image=movImage,
transform=transform[0],
center=transform[1],
reference_image=refImage,
order=3)
nibabel.save(outputImage, args.output)
