HList.append(
compute_H(inputImages[i], initHRImage, inputTransforms[i],
psfList[i], maskImages[i]))
#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
HList = []
for i in range(len(inputImages)):
HList.append(compute_H(inputImages[i], initHRImage, inputTransforms[i], psfList[i], maskImages[i]))
# 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
import sys
import argparse
from os import path
import nibabel
import numpy as np
sys.path.append( path.dirname( path.dirname( path.dirname( path.abspath(__file__) ) ) ) )
from pybtk.filters.imagefilters import gaussian_biais_correction
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', help='Moving image filename (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)
parser.add_argument('-s', '--sigma', help='Sigma of Gaussian filtering ', type=float, default=5)
args = parser.parse_args()
inpImage = nibabel.load(args.input)
refImage = nibabel.load(args.ref)
outputImage = gaussian_biais_correction(input_image=inpImage,reference_image=refImage, sigma=args.sigma)
nibabel.save(outputImage,args.output)
parser.add_argument('-i',
'--input',
help='Moving image filename (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)
parser.add_argument('-s',
'--sigma',
help='Sigma of Gaussian filtering ',
type=float,
default=5)
args = parser.parse_args()
inpImage = nibabel.load(args.input)
refImage = nibabel.load(args.ref)
outputImage = gaussian_biais_correction(input_image=inpImage,
reference_image=refImage,
sigma=args.sigma)
nibabel.save(outputImage, args.output)
