def VisualizeSpectra(rf_files, freq_sampling):
plt.figure(1, figsize=(20, 16))
for rf_file in rf_files:
image = itk.imread(rf_file)
arr = itk.GetArrayViewFromImage(image)
freq, Pxx = signal.periodogram(arr.transpose(),
freq_sampling,
window='hamming',
detrend='linear',
axis=0)
Pxx = np.mean(Pxx, 1)
plt.semilogy(freq, Pxx, label=rf_file)
plt.xlabel('Frequency [Hz]')
plt.ylabel('Power spectral density [V**2/Hz]')
plt.legend(loc='upper right')
dirname = os.path.dirname(rf_files[0])
plt.savefig(os.path.join(dirname, 'PowerSpectralDensity.png'), dpi=300)
plt.show()
assert s.GetSize()[0] == s.GetSize()[1] == 256
s = itk.region(reader.GetOutput())
assert s.GetIndex()[0] == s.GetIndex()[1] == 0
assert s.GetSize()[0] == s.GetSize()[1] == 256
# test range
assert itk.range(reader) == (0, 255)
assert itk.range(reader.GetOutput()) == (0, 255)
# test write
itk.imwrite(reader, sys.argv[3])
itk.imwrite(reader, sys.argv[3], True)
# test read
image = itk.imread(filename)
assert type(image) == itk.Image[itk.RGBPixel[itk.UC], 2]
image = itk.imread(filename, itk.F)
assert type(image) == itk.Image[itk.F, 2]
image = itk.imread(filename, itk.F, fallback_only=True)
assert type(image) == itk.Image[itk.RGBPixel[itk.UC], 2]
try:
image = itk.imread(filename, fallback_only=True)
# Should never reach this point if test passes since an exception
# is expected.
raise Exception("`itk.imread()` fallback_only should have failed")
except Exception as e:
if str(e) == "pixel_type must be set when using the fallback_only option":
pass
else:
raise e
import itk
import sys
if len(sys.argv) != 6:
print(
f"Usage: {sys.argv[0]} input_image_file spacing_fraction sigma_fraction output_image_file_label_image_interpolator output_image_file_nearest_neighbor_interpolator"
)
sys.exit(1)
input_image_file = sys.argv[1]
spacing_fraction = float(sys.argv[2])
sigma_fraction = float(sys.argv[3])
output_image_file_label_image_interpolator = sys.argv[4]
output_image_file_nearest_neighbor_interpolator = sys.argv[5]
input_image = itk.imread(input_image_file)
resize_filter = itk.ResampleImageFilter.New(input_image)
input_spacing = itk.spacing(input_image)
output_spacing = [s * spacing_fraction for s in input_spacing]
resize_filter.SetOutputSpacing(output_spacing)
input_size = itk.size(input_image)
output_size = [
int(s * input_spacing[dim] / spacing_fraction) for dim, s in enumerate(input_size)
]
resize_filter.SetSize(output_size)
gaussian_interpolator = itk.LabelImageGaussianInterpolateImageFunction.New(input_image)
sigma = [s * sigma_fraction for s in output_spacing]
def generate_val_sample(image_list, h5_path, patch_size):
# printing process id
print('PID: {0}, number of samples: {1}'.format(os.getpid(),
len(image_list)))
for idx in range(image_list.shape[0]):
i_sample = image_list.iloc[idx]['ID']
print('PID: {0} -- '.format(os.getpid()),
image_list.iloc[idx]['image'][:])
# read image
itk_image = itk.imread(image_list.iloc[idx]['image'])
itk_annotation = itk.imread(image_list.iloc[idx]['label'])
np_image = itk.array_from_image(itk_image)
np_annotation = itk.array_from_image(itk_annotation)
# normalized
#np_image = (np_image - np_image.mean())/ np_image.std()
np_image = np_image / (np_image.max() - np_image.min())
# reshape
np_image = np_image.reshape(
[1, np_image.shape[0], np_image.shape[1], np_image.shape[2]])
np_annotation = np_annotation.reshape([
1, np_annotation.shape[0], np_annotation.shape[1],
np_annotation.shape[2]
])
tensor_image = torch.from_numpy(np_image)
tensor_annotation = torch.from_numpy(np_annotation)
# get patches with proper strides to slide all image
image_patches = tensor_image.unfold(
1, patch_size[0],
get_stride(np_image.shape[1], patch_size[0])).unfold(
2, patch_size[1],
get_stride(np_image.shape[2], patch_size[1])).unfold(
3, patch_size[2],
get_stride(np_image.shape[3], patch_size[2]))
annotation_patches = tensor_annotation.unfold(
1, patch_size[0],
get_stride(np_image.shape[1], patch_size[0])).unfold(
2, patch_size[1],
get_stride(np_image.shape[2], patch_size[1])).unfold(
3, patch_size[2],
get_stride(np_image.shape[3], patch_size[2]))
image_patches = image_patches.reshape(-1, 1, patch_size[0],
patch_size[1], patch_size[2])
annotation_patches = annotation_patches.reshape(
-1, 1, patch_size[0], patch_size[1], patch_size[2])
patch_image = image_patches.numpy()
patch_label = annotation_patches.numpy()
# save to h5
if not os.path.exists(h5_path):
os.makedirs(h5_path)
for i_patch in range(image_patches.shape[0]):
patch_file_name = os.path.join(
h5_path, 'val_sample_{0}_patch_{1}x{2}x{3}_{4}.h5'.format(
i_sample, patch_size[0], patch_size[1], patch_size[2],
i_patch))
#check old patch file
if os.path.isfile(patch_file_name):
os.remove(patch_file_name)
#output h5 file
with h5py.File(patch_file_name, 'w') as f:
f['image'] = patch_image[i_patch, :, :, :, :]
f['label'] = patch_label[i_patch, :, :, :, :]
def copy_images(images_test, task_id, task_name_3D, patient_ind,
testimage_name, ROI_list, ROI_list_alternatenames, label_dict):
image_identifier_3D = 'CBCT_3D'
#realCBCT_N4imagename = 'realCBCT_correctedN4_beforeshifting.mha'
# copy the 3D cbct images to imagesTr in .nii.gz format
global taskfolder_3D
taskfolder_3D = f'{nnUNet_raw_data}/Task{task_id:03d}_{task_name_3D}'
json_filename_3D = f'{taskfolder_3D}/dataset.json'
imagesTs_foldername_3D = f'{taskfolder_3D}/imagesTs/'
labelsTs_foldername_3D = f'{taskfolder_3D}/labelsTs/'
maybe_mkdir_p(imagesTs_foldername_3D)
maybe_mkdir_p(labelsTs_foldername_3D)
open(f"{taskfolder_3D}/errorlog_test.txt", "w").close()
test_patient_names = []
for patient_folder in sorted(glob.glob(f'{images_test}/*')):
# if rennes images, the name of the cbct is different and there are more than one cbct per patient
if 'Rennes' in patient_folder:
#cbct_image_path = glob.glob(f'{patient_folder}/cbct*.nii.gz')[0]
if 'pseudo' in patient_folder:
image_prefix = 'pseudo_rec'
else:
image_prefix = 'cbct'
if (apply_matching == False) & ('correctedN4' in testimage_name):
for cbct_image_path in sorted(
glob.glob(
f'{patient_folder}/{image_prefix}*_N4folder/{realCBCT_N4imagename}'
)):
cbct_id = os.path.dirname(cbct_image_path).replace(
f'{patient_folder}/{image_prefix}',
'').replace('_N4folder', '')
with open(f'{taskfolder_3D}/errorlog_test.txt', 'a') as f:
f.write(
str(patient_ind) + " - " + patient_folder +
" - cbct id " + cbct_id + " : \n")
print(patient_folder)
casename_3D = f'{image_identifier_3D}_{patient_ind:04d}'
test_patient_names.append(casename_3D)
cbct_3D_filename = f'{imagesTs_foldername_3D}/{casename_3D}_0000.nii.gz'
if spacing2mm == True:
cbct_img = itk.imread(cbct_image_path)
img_origin = cbct_img.GetOrigin()
new_cbct_img = gt.applyTransformation(
input=cbct_img,
neworigin=img_origin,
newspacing=img_spacing,
adaptive=True,
force_resample=True)
itk.imwrite(new_cbct_img, cbct_3D_filename)
else:
convert_command = f'gt_image_convert {cbct_image_path} -o {cbct_3D_filename}'
os.system(convert_command)
# copy and register label (ROI) with the cbct
ROI_dir = f'{patient_folder}/ROI'
allRoisImage = create_segmentation_map(
ROI_list, ROI_list_alternatenames, ROI_dir,
cbct_3D_filename, cbct_id)
label_3D_filename = f'{labelsTs_foldername_3D}/{casename_3D}.nii.gz'
itk.imwrite(allRoisImage, label_3D_filename)
patient_ind = patient_ind + 1
elif (apply_matching == False) & ('swn_normalized'
in testimage_name):
for cbct_image_path in sorted(
glob.glob(
f'{patient_folder}/{image_prefix}*_SWNfolder/{realCBCT_SWNimagename}'
)):
cbct_id = os.path.dirname(cbct_image_path).replace(
f'{patient_folder}/{image_prefix}',
'').replace('_SWNfolder', '')
with open(f'{taskfolder_3D}/errorlog_test.txt', 'a') as f:
f.write(
str(patient_ind) + " - " + patient_folder +
" - cbct id " + cbct_id + " : \n")
print(patient_folder)
casename_3D = f'{image_identifier_3D}_{patient_ind:04d}'
test_patient_names.append(casename_3D)
cbct_3D_filename = f'{imagesTs_foldername_3D}/{casename_3D}_0000.nii.gz'
if spacing2mm == True:
cbct_img = itk.imread(cbct_image_path)
img_origin = cbct_img.GetOrigin()
new_cbct_img = gt.applyTransformation(
input=cbct_img,
neworigin=img_origin,
newspacing=img_spacing,
adaptive=True,
force_resample=True)
itk.imwrite(new_cbct_img, cbct_3D_filename)
else:
convert_command = f'gt_image_convert {cbct_image_path} -o {cbct_3D_filename}'
os.system(convert_command)
# copy and register label (ROI) with the cbct
ROI_dir = f'{patient_folder}/ROI'
allRoisImage = create_segmentation_map(
ROI_list, ROI_list_alternatenames, ROI_dir,
cbct_3D_filename, cbct_id)
label_3D_filename = f'{labelsTs_foldername_3D}/{casename_3D}.nii.gz'
itk.imwrite(allRoisImage, label_3D_filename)
patient_ind = patient_ind + 1
else:
for cbct_image_path in sorted(
glob.glob(f'{patient_folder}/{image_prefix}*.nii.gz')):
cbct_id = os.path.basename(cbct_image_path).replace(
f'{image_prefix}', '').replace('.nii.gz', '')
with open(f'{taskfolder_3D}/errorlog_test.txt', 'a') as f:
f.write(
str(patient_ind) + " - " + patient_folder +
" - cbct id " + cbct_id + " : \n")
print(patient_folder)
casename_3D = f'{image_identifier_3D}_{patient_ind:04d}'
test_patient_names.append(casename_3D)
# convert to float
convert_command = f'clitkImageConvert -i {cbct_image_path} -o {cbct_image_path} -t float'
os.system(convert_command)
# reduce image size and save in 3D_outputfolder
cbct_3D_filename = f'{imagesTs_foldername_3D}/{casename_3D}_0000.nii.gz'
if spacing2mm == True:
cbct_img = itk.imread(cbct_image_path)
img_origin = cbct_img.GetOrigin()
new_cbct_img = gt.applyTransformation(
input=cbct_img,
neworigin=img_origin,
newspacing=img_spacing,
adaptive=True,
force_resample=True)
itk.imwrite(new_cbct_img, cbct_3D_filename)
else:
convert_command = f'gt_image_convert {cbct_image_path} -o {cbct_3D_filename}'
os.system(convert_command)
# copy and register label (ROI) with the cbct
ROI_dir = f'{patient_folder}/ROI'
allRoisImage = create_segmentation_map(
ROI_list, ROI_list_alternatenames, ROI_dir,
cbct_3D_filename, cbct_id)
label_3D_filename = f'{labelsTs_foldername_3D}/{casename_3D}.nii.gz'
itk.imwrite(allRoisImage, label_3D_filename)
patient_ind = patient_ind + 1
else:
with open(f'{taskfolder_3D}/errorlog_test.txt', 'a') as f:
f.write(str(patient_ind) + " - " + patient_folder + ": \n")
print(patient_folder)
casename_3D = f'{image_identifier_3D}_{patient_ind:04d}'
test_patient_names.append(casename_3D)
if 'CLB' in patient_folder:
if (apply_matching == False) & ('correctedN4'
in testimage_name):
cbct_image_path = f'{patient_folder}/{realCBCT_N4imagename}'
elif (apply_matching == False) & ('swn_normalized'
in testimage_name):
cbct_image_path = f'{patient_folder}/{realCBCT_SWNimagename}'
else:
cbct_image_path = glob.glob(
f'{patient_folder}/cbct*.mhd')[0]
else:
cbct_image_path = f'{patient_folder}/{testimage_name}'
#cbct_image_path = f'{patient_folder}/{testimage_name}'
# reduce image size and save in 3D_outputfolder
cbct_3D_filename = f'{imagesTs_foldername_3D}/{casename_3D}_0000.nii.gz'
#if (apply_matching == False) & ('correctedN4' in testimage_name): # no need to resize, already 2 mm spacing
# #shutil.copy(cbct_image_path, imagesTr_foldername_3D)
# #os.rename(f'{imagesTr_foldername_3D}/{trainingimage_name}', cbct_3D_filename)
# convert_command= f'gt_image_convert {cbct_image_path} -o {cbct_3D_filename}'
#else:
# convert_command= f'gt_affine_transform -i {cbct_image_path} --newspacing=2,2,2 -o {cbct_3D_filename} -fr -a'
if spacing2mm == True:
convert_command = f'gt_affine_transform -i {cbct_image_path} --newspacing=2,2,2 -o {cbct_3D_filename} -fr -a'
os.system(convert_command)
else:
convert_command = f'gt_image_convert {cbct_image_path} -o {cbct_3D_filename}'
os.system(convert_command)
# copy and register label (ROI) with the cbct
ROI_dir = f'{patient_folder}/ROI'
allRoisImage = create_segmentation_map(ROI_list,
ROI_list_alternatenames,
ROI_dir, cbct_3D_filename,
'')
label_3D_filename = f'{labelsTs_foldername_3D}/{casename_3D}.nii.gz'
itk.imwrite(allRoisImage, label_3D_filename)
patient_ind = patient_ind + 1
# dataset.json 3D
json_dict = {}
json_dict['name'] = task_name_3D
json_dict['description'] = ""
json_dict['tensorImageSize'] = "4D"
json_dict['reference'] = "saphir"
json_dict['licence'] = ""
json_dict['release'] = "0.0"
json_dict['modality'] = {
"0": "CT",
}
json_dict['labels'] = label_dict
json_dict['numTest'] = len(test_patient_names)
json_dict['test'] = [
"./imagesTs/%s.nii.gz" % i for i in test_patient_names
]
save_json(json_dict, json_filename_3D)
assert s.GetSize()[0] == s.GetSize()[1] == 256
s = itk.region(reader.GetOutput())
assert s.GetIndex()[0] == s.GetIndex()[1] == 0
assert s.GetSize()[0] == s.GetSize()[1] == 256
# test range
assert itk.range(reader) == (0, 255)
assert itk.range(reader.GetOutput()) == (0, 255)
# test write
itk.imwrite(reader, sys.argv[4])
itk.imwrite(reader, sys.argv[4], imageio=itk.PNGImageIO.New())
itk.imwrite(reader, sys.argv[4], True)
# test read
image = itk.imread(pathlib.Path(filename))
assert type(image) == itk.Image[itk.RGBPixel[itk.UC], 2]
image = itk.imread(filename, itk.F)
assert type(image) == itk.Image[itk.F, 2]
image = itk.imread(filename, itk.F, fallback_only=True)
assert type(image) == itk.Image[itk.RGBPixel[itk.UC], 2]
try:
image = itk.imread(filename, fallback_only=True)
# Should never reach this point if test passes since an exception
# is expected.
raise Exception("`itk.imread()` fallback_only should have failed")
except Exception as e:
if str(e) == "pixel_type must be set when using the fallback_only option":
pass
else:
raise e
import itk
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
im=itk.imread('data/CBCT-TextureInput.png', itk.F)
sobel=itk.sobel_edge_detection_image_filter(im)
arr = itk.array_from_image(sobel)
plt.gray()
plt.imshow(arr)
plt.axis('off')
def __init__(self,mhd,meta_data={}):
self._meta_data = meta_data
self._mhd = mhd
self._img = itk.imread(mhd)
self._slices = []
def load_data(self, path_infos, indices, train=False):
mini_batch_size = len(indices)
in_channels = 1
if train == True:
xs = np.zeros((mini_batch_size, in_channels, self.crop_size,
self.crop_size, self.crop_size)).astype(np.float32)
ys = np.zeros((mini_batch_size, in_channels, self.crop_size,
self.crop_size, self.crop_size)).astype(np.float32)
for i, index in enumerate(indices):
path = path_infos[index]
labelPath = self.labelPathFromPath(path)
affPath, warpPath, invWarpPath = self.transformPathsFromPath(
path)
labelAffPath, labelWarpPath, labelInvWarpPath = self.transformPathsFromPath(
labelPath)
atlasPath = self.atlasPathFromPath(path)
img = np.array(nib.load(path).dataobj)
labelImg = np.array(nib.load(labelPath).dataobj)
imgAff = np.linalg.inv(self.antsmat2mat(io.loadmat(affPath)))
labelAff = self.antsmat2mat(io.loadmat(labelAffPath))
imgInvWarp = np.array(nib.load(invWarpPath).dataobj)
labelWarp = np.array(nib.load(labelWarpPath).dataobj)
imgITK = itk.imread(path)
labelITK = itk.imread(labelPath)
atlasITK = itk.imread(atlasPath)
if img is None or labelImg is None:
raise RuntimeError("invalid image: {i}".format(i=path))
x, y, z = img.shape
# Restrict random cropping to a central location in the image
# to avoid training on too much blank space
xRange = 20
yRange = 70
zRange = 70
rand_range_x = x - self.crop_size - (xRange * 2)
rand_range_y = y - self.crop_size - (yRange * 2)
rand_range_z = z - self.crop_size - (zRange * 2)
x_offset = np.random.randint(rand_range_x) + xRange
y_offset = np.random.randint(rand_range_y) + yRange
z_offset = np.random.randint(rand_range_z) + zRange
img = img[x_offset:x_offset + self.crop_size,
y_offset:y_offset + self.crop_size,
z_offset:z_offset + self.crop_size]
# Get a transformed patch of the label image to match
# the training image
labelImgWarped = np.zeros(
(self.crop_size, self.crop_size, self.crop_size))
for i in range(self.crop_size):
for j in range(self.crop_size):
for k in range(self.crop_size):
originPoint = [
x_offset + i, y_offset + j, z_offset + k
]
refPoint = self.transformPoint(
originPoint, imgAff, labelAff, imgInvWarp,
labelWarp, imgITK, labelITK, atlasITK)
labelImgWarped[i, j, k] = labelImg[refPoint[0],
refPoint[1],
refPoint[2]]
labelImg = labelImgWarped
# Normalize images
if img.max() > 0 and labelImg.max() > 0:
img = img.astype(np.float32)
labelImg = labelImg.astype(np.float32)
img = (img / img.max())
labelImg = (labelImg / labelImg.max())
else:
img = np.zeros(img.shape)
labelImg = np.zeros(labelImg.shape)
xs[i, 0, :, :, :] = img.astype(np.float32)
ys[i, 0, :, :, :] = labelImg.astype(np.float32)
return xs, ys
else:
for i, index in enumerate(indices):
path = path_infos[index]
imgNib = nib.load(path)
imgAffine = imgNib.affine
img = np.array(imgNib.dataobj)
if img is None:
raise RuntimeError("invalid image: {i}".format(i=path))
img = img.astype(np.float32)
maxIntensity = img.max()
# Normalize image
x, y, z = img.shape
xs = np.zeros(
(mini_batch_size, in_channels, x, y, z)).astype(np.float32)
xs[0, 0, :, :, :] = (img / img.max()).astype(np.float32)
imgFileName = os.path.splitext(os.path.basename(path))[0]
return xs, maxIntensity, imgFileName, imgAffine
labels_np[labels_np > 7] = 0
if __name__ == '__main__':
# TODO: set to True for CT and False for MR
is_ct = True
# TODO: change input folder
input_folder = 'TODO'
output_folder = './mmwhs_dataset/ct_mha/' if is_ct else './mmwhs_dataset/mr_mha/'
if not os.path.exists(output_folder):
os.makedirs(output_folder)
filenames = glob(input_folder + '*image.nii.gz')
for filename in sorted(filenames):
basename = os.path.basename(filename)
basename_wo_ext = basename[:basename.find('.nii.gz')]
print(basename_wo_ext)
image = itk.imread(filename)
reoriented = reorient_to_rai(image)
itk.imwrite(reoriented, output_folder + basename_wo_ext + '.mha')
filenames_label = glob(input_folder + '*label.nii.gz')
for filename in sorted(filenames):
basename = os.path.basename(filename)
basename_wo_ext = basename[:basename.find('.nii.gz')]
print(basename_wo_ext)
image = itk.imread(filename)
reoriented = reorient_to_rai(image)
relabel(reoriented)
itk.imwrite(reoriented,
output_folder + basename_wo_ext + '_sorted.mha')
# limitations under the License.
import argparse
import itk
from distutils.version import StrictVersion as VS
if VS(itk.Version.GetITKVersion()) < VS("5.0.0"):
print("ITK 5.0.0 or newer is required.")
sys.exit(1)
parser = argparse.ArgumentParser(description='Segment blood vessels.')
parser.add_argument('input_image')
parser.add_argument('output_image')
parser.add_argument('--sigma', type=float, default=1.0)
parser.add_argument('--alpha1', type=float, default=0.5)
parser.add_argument('--alpha2', type=float, default=2.0)
args = parser.parse_args()
input_image = itk.imread(args.input_image, itk.ctype('float'))
hessian_image = itk.hessian_recursive_gaussian_image_filter(input_image,
sigma=args.sigma)
vesselness_filter = itk.Hessian3DToVesselnessMeasureImageFilter[itk.ctype(
'float')].New()
vesselness_filter.SetInput(hessian_image)
vesselness_filter.SetAlpha1(args.alpha1)
vesselness_filter.SetAlpha2(args.alpha2)
itk.imwrite(vesselness_filter, args.output_image)
import itk
from distutils.version import StrictVersion as VS
if VS(itk.Version.GetITKVersion()) < VS("5.0.0"):
print("ITK 5.0.0 or newer is required.")
sys.exit(1)
parser = argparse.ArgumentParser(description='Segment blood vessels with multi-scale Hessian-based measure.')
parser.add_argument('input_image')
parser.add_argument('output_image')
parser.add_argument('--sigma_minimum', type=float, default=1.0)
parser.add_argument('--sigma_maximum', type=float, default=10.0)
parser.add_argument('--number_of_sigma_steps', type=int, default=10)
args = parser.parse_args()
input_image = itk.imread(args.input_image, itk.F)
ImageType = type(input_image)
Dimension = input_image.GetImageDimension()
HessianPixelType = itk.SymmetricSecondRankTensor[itk.D, Dimension]
HessianImageType = itk.Image[HessianPixelType, Dimension]
objectness_filter = itk.HessianToObjectnessMeasureImageFilter[HessianImageType, ImageType].New()
objectness_filter.SetBrightObject(False)
objectness_filter.SetScaleObjectnessMeasure(False)
objectness_filter.SetAlpha(0.5)
objectness_filter.SetBeta(1.0)
objectness_filter.SetGamma(5.0)
multi_scale_filter = itk.MultiScaleHessianBasedMeasureImageFilter[ImageType, HessianImageType, ImageType].New()
multi_scale_filter.SetInput(input_image)
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0.txt
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
#==========================================================================*/
from __future__ import print_function
import itk
from sys import argv, stderr, exit
itk.auto_progress(2)
if(len(argv) < 2):
print((
"Missing Parameters \n Usage: IntensityWindowingImageFilter.py inputImageFile"), file=stderr)
exit(1)
image = itk.imread(argv[1], itk.F)
# Verifies that ITK supports getting tuples for filter parameters. Not testing the filter
# results.
intensity_filter = itk.IntensityWindowingImageFilter.New(image, WindowLevel=[255, 127])
#========================================================================== # # Copyright Insight Software Consortium # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0.txt # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # #==========================================================================*/ # # Example on the use of the MeanImageFilter with function calls # import itk from sys import argv image = itk.imread(argv[1]) filtered_image = itk.MeanImageFilter(image, Radius=int(argv[3])) itk.imwrite(filtered_image, argv[2])
assert s.GetIndex()[0] == s.GetIndex()[1] == 0
assert s.GetSize()[0] == s.GetSize()[1] == 256
# test range
assert itk.range(reader) == (0, 255)
assert itk.range(reader.GetOutput()) == (0, 255)
# test write
itk.imwrite(reader, sys.argv[2])
itk.write(reader, sys.argv[2])
itk.imwrite(reader, sys.argv[2], True)
# test read
image=itk.imread(fileName)
assert type(image) == itk.Image[itk.RGBPixel[itk.UC],2]
image=itk.imread(fileName, itk.F)
assert type(image) == itk.Image[itk.F,2]
# test search
res = itk.search("Index")
assert res[0] == "Index"
assert res[1] == "index"
assert "ContinuousIndex" in res
res = itk.search("index", True)
assert "Index" not in res
# test down_cast
assert s.GetIndex()[0] == s.GetIndex()[1] == 0
assert s.GetSize()[0] == s.GetSize()[1] == 256
# test range
assert itk.range(reader) == (0, 255)
assert itk.range(reader.GetOutput()) == (0, 255)
# test write
itk.imwrite(reader, sys.argv[2])
itk.write(reader, sys.argv[2])
itk.imwrite(reader, sys.argv[2], True)
# test read
image=itk.imread(fileName)
assert type(image) == itk.Image[itk.RGBPixel[itk.UC],2]
image=itk.imread(fileName, itk.F)
assert type(image) == itk.Image[itk.F,2]
# test search
res = itk.search("Index")
assert res[0] == "Index"
assert res[1] == "index"
assert "ContinuousIndex" in res
res = itk.search("index", True)
assert "Index" not in res
# test down_cast
<<<<<<< HEAD
=======
>>>>>>> be773a0d454a339658ba66aa1e901b20108099e9
fixedImageFile = "fixed.png"
movingImageFile = "moving.png"
outputImageFile = "output.png"
differenceImageAfterFile = "after.png"
differenceImageBeforeFile = "before.png"
PixelType = itk.ctype('float')
fixedImage = itk.imread(fixedImageFile, PixelType)
movingImage = itk.imread(movingImageFile, PixelType)
Dimension = fixedImage.GetImageDimension()
FixedImageType = itk.Image[PixelType, Dimension]
MovingImageType = itk.Image[PixelType, Dimension]
TransformType = itk.TranslationTransform[itk.D, Dimension]
initialTransform = TransformType.New()
optimizer = itk.RegularStepGradientDescentOptimizerv4.New(LearningRate=4,MinimumStepLength=0.001, RelaxationFactor=0.5, NumberOfIterations=200)
metric = itk.MeanSquaresImageToImageMetricv4[FixedImageType, MovingImageType].New()
registration = itk.ImageRegistrationMethodv4.New(FixedImage=fixedImage,
MovingImage=movingImage, Metric=metric, Optimizer=optimizer,
InitialTransform=initialTransform)
#def main():
fns = glob(os.path.join(img_dir, '*.nii*'))
for fn in fns:
_, base, ext = split_filename(fn)
img = nib.load(fn).get_data().astype(np.float32).squeeze()
if img.ndim != 3:
print(
f'Only 3D data supported. File {base}{ext} has dimension {img.ndim}. Skipping.'
)
continue
input_filename = fn
reader = itk.imread(input_filename, itk.F)
reader.Update()
image = reader
if (img.shape[0] % int(x_shape)) != 0:
print(
f'File {base}{ext} does not ensure equal split of input image along x axis'
)
continue
if (img.shape[1] % int(y_shape)) != 0:
print(
f'File {base}{ext} does not ensure equal split of input image along y axis'
)
continue
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import itk
from distutils.version import StrictVersion as VS
if VS(itk.Version.GetITKVersion()) < VS("5.0.0"):
print("ITK 5.0.0 or newer is required.")
sys.exit(1)
parser = argparse.ArgumentParser(description='Segment blood vessels.')
parser.add_argument('input_image')
parser.add_argument('output_image')
parser.add_argument('--sigma', type=float, default=1.0)
parser.add_argument('--alpha1', type=float, default=0.5)
parser.add_argument('--alpha2', type=float, default=2.0)
args = parser.parse_args()
input_image = itk.imread(args.input_image, itk.ctype('float'))
hessian_image = itk.hessian_recursive_gaussian_image_filter(input_image, sigma=args.sigma)
vesselness_filter = itk.Hessian3DToVesselnessMeasureImageFilter[itk.ctype('float')].New()
vesselness_filter.SetInput(hessian_image)
vesselness_filter.SetAlpha1(args.alpha1)
vesselness_filter.SetAlpha2(args.alpha2)
itk.imwrite(vesselness_filter, args.output_image)
from __future__ import print_function
import sys
try:
import numpy as np
except ImportError:
# We don't have numpy -- bail
sys.exit(0)
import itk
if len(sys.argv) < 2:
print('Usage: ' + sys.argv[0] + ' <inputImage>')
sys.exit(1)
inputImageFileName = sys.argv[1]
image = itk.imread(inputImageFileName)
array = itk.GetArrayFromImage(image)
extractor = itk.ExtractImageFilter.New(image)
extractionRegion = image.GetLargestPossibleRegion()
extractor.SetExtractionRegion(extractionRegion)
# GetArrayFromImage calls UpdateLargestPossibleRegion to ensure the image buffer
# has been populated
array = itk.GetArrayFromImage(extractor.GetOutput())
# GetArrayFromImage calls UpdateLargestPossibleRegion to ensure the image buffer
# has been populated with the correct region
extractionRegion.SetSize(10)
extractor.SetExtractionRegion(extractionRegion)
array = itk.GetArrayFromImage(extractor.GetOutput())
def read(self,
filename,
intensity_normalize=False,
squeeze_image=False,
normalize_spacing=True,
adaptive_padding=-1,
verbose=False,
silent_mode=False):
"""
Reads the image assuming it is a single channel
:param filename: filename to be read
:param intensity_normalize: uses image intensity normalization
:param squeeze_image: squeezes image first (e.g, from 1x128x128 to 128x128)
:param normalize_spacing: normalizes spacing so largest extent is in [0,1]
:param silent_mode: if True, suppresses output
:return: Will return the read file, its header information, the spacing, and the normalized spacing \
(as a tuple: im,hdr,spacing,squeezed_spacing)
"""
self.set_intensity_normalization(intensity_normalize)
self.set_squeeze_image(squeeze_image)
self.set_adaptive_padding(adaptive_padding)
self.set_normalize_spacing(normalize_spacing)
if verbose and not silent_mode:
print('Reading image: ' + filename)
# read with the itk reader (can also read other file formats)
im_itk = itk.imread(native_str(filename))
im, hdr = self._convert_itk_image_to_numpy(im_itk)
if self.replace_nans_with_zeros:
im[np.isnan(im)] = 0
if self.datatype_conversion:
im = im.astype(self.default_datatype)
if 'spacing' not in hdr:
if not silent_mode:
print('Image does not seem to have spacing information.')
if 'sizes' in hdr:
dim_guess = len(hdr['sizes'])
else:
dim_guess = len(im.shape)
if not silent_mode:
print('Guessed dimension to be dim = ' + str(dim_guess))
spacing = np.ones(dim_guess)
hdr['spacing'] = spacing
if not silent_mode:
print('Using guessed spacing of ' + str(spacing))
spacing = np.flipud(hdr['spacing'])
squeezed_spacing = spacing # will be changed if image is squeezed
sz = im.shape
sz_squeezed = sz
if self.squeeze_image == True:
if verbose and not silent_mode:
print('Squeezing image')
if hdr['is_vector_image']:
dim = len(im.shape[1:])
else:
dim = len(im.shape)
im = im.squeeze()
if hdr['is_vector_image']:
dimSqueezed = len(im.shape[1:])
else:
dimSqueezed = len(im.shape)
hdr['squeezed_dim'] = dimSqueezed
sz_squeezed = im.shape
if dim != dimSqueezed:
if verbose and not silent_mode:
print('Squeezing changed dimension from ' + str(dim) +
' -> ' + str(dimSqueezed))
if hdr['is_vector_image']:
squeezed_spacing = self._compute_squeezed_spacing(
spacing, dim, sz[1:], dimSqueezed)
else:
squeezed_spacing = self._compute_squeezed_spacing(
spacing, dim, sz, dimSqueezed)
if verbose and not silent_mode:
print('squeezed_spacing = ' + str(squeezed_spacing))
#squeezed_spacing = squeezed_spacing / (np.array(sz_squeezed) - 1)
#if verbose and not silent_mode:
# print('Normalized spacing = ' + str(squeezed_spacing))
if adaptive_padding > 0:
im = self._do_adaptive_padding(im)
if self.intensity_normalize_image == True:
im = IM.IntensityNormalizeImage().default_intensity_normalization(
im)
if not silent_mode:
print('INFO: Image WAS intensity normalized when loading:' \
+ ' [' + str(im.min()) + ',' + str(im.max()) + ']')
else:
if not silent_mode:
print('WARNING: Image was NOT intensity normalized when loading:' \
+ ' [' + str(im.min()) + ',' + str(im.max()) + ']')
if self.normalize_spacing:
if not silent_mode:
print(
'INFO: Normalizing the spacing to [0,1] in the largest dimension. (Turn normalize_spacing off if this is not desired.)'
)
hdr['original_spacing'] = spacing
if hdr['is_vector_image']:
spacing = self._normalize_spacing(spacing, sz[1:], silent_mode)
squeezed_spacing = self._normalize_spacing(
squeezed_spacing, sz_squeezed[1:], silent_mode)
else:
spacing = self._normalize_spacing(spacing, sz, silent_mode)
squeezed_spacing = self._normalize_spacing(
squeezed_spacing, sz_squeezed, silent_mode)
hdr['spacing'] = spacing
if verbose and not silent_mode:
print('Normalized spacing = ' + str(spacing))
print('Normalized squeezed spacing = ' + str(squeezed_spacing))
if hdr['is_vector_image']:
if self.scale_vectors_on_read_and_write:
hdr['vector_image_was_scaled'] = True
if not silent_mode:
print(
'Scaling the vector image to conform to the scaled spacing'
)
# we also need to normalize the vector components in this case
vector_scaling = np.array(hdr['spacing']) / np.array(
hdr['original_spacing'])
for d in range(dim):
im[d, ...] *= vector_scaling[d]
return im, hdr, spacing, squeezed_spacing
import os
import dash
import dash_html_components as html
import itk
import dash_vtk
from dash_vtk.utils import to_volume_state
# Place a DICOM series (a set of per-file slices) in a directory. ITK sorts, sets spatial metadata, etc.
demo_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
itk_image = itk.imread(os.path.join(demo_dir, "data", "ct_lung"))
# Convert itk.Image to vtkImageData
vtk_image = itk.vtk_image_from_image(itk_image)
volume_state = to_volume_state(vtk_image)
vtk_view = dash_vtk.View(
dash_vtk.VolumeRepresentation(
children=[dash_vtk.VolumeController(), dash_vtk.Volume(state=volume_state),]
)
)
app = dash.Dash(__name__)
server = app.server
app.layout = html.Div(
style={"height": "calc(100vh - 50px)", "width": "100%"},
children=[html.Div(vtk_view, style={"height": "100%", "width": "100%"})],
)
#!/usr/bin/env python3 import numpy as np import itk import sys input_filename = sys.argv[1] output_filename = sys.argv[2] image = itk.imread(input_filename) # Cast to an unsigned char pixel type cast_image = image.astype(itk.UC) # Equivalent cast_image = image.astype(np.uint8) itk.imwrite(cast_image, output_filename)
img_count += 1
if img_count > num_imgs:
break
if filename.endswith(".nii.gz"):
input_filename = os.path.join(input_dir, filename)
else:
continue
verbose = False # verbose details of all steps.
#% -------------------- Reader -------------------------
InputImageType = get_itk_image_type(input_filename)
print(InputImageType)
OutputImageType = InputImageType
inputImage = itk.imread(input_filename, itk.SS)
#%% Set input information
sizeOutput = [1024, 1400,
1] # The size of output image (originally [1024,1400,1])
threshold = 0.
rot = [0., 0., 0.] # rotation in degrees in x, y, and z direction.
t = [-230, -350, 1500.] # translation in x, y, and z directions.
cor = [0., 0., 0.] # offset of the rotation from the center of image (3D)
spaceOutput = [0.167, 0.167, 1]
delta = sizeOutput[0] * spaceOutput[0] / 2
inputImage.SetOrigin(
[0, 0, 0]) # set the origin to (0,0,0) to fix translated image problem
#!/usr/bin/env python
# Copyright NumFOCUS
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0.txt
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import itk
import argparse
parser = argparse.ArgumentParser(description="Sobel Edge Detection Image Filter.")
parser.add_argument("input_image")
parser.add_argument("output_image")
args = parser.parse_args()
input_image = itk.imread(args.input_image, pixel_type=itk.F)
output_image = itk.sobel_edge_detection_image_filter(input_image)
itk.imwrite(output_image, args.output_image)
#==========================================================================*/
import sys
try:
import numpy as np
except ImportError:
# We don't have numpy -- bail
sys.exit(0)
import itk
if len(sys.argv) < 2:
print('Usage: ' + sys.argv[0] + ' <inputImage>')
sys.exit(1)
inputImageFileName = sys.argv[1]
image = itk.imread(inputImageFileName)
array = itk.GetArrayFromImage(image)
extractor = itk.ExtractImageFilter.New(image)
extractionRegion = image.GetLargestPossibleRegion()
extractor.SetExtractionRegion(extractionRegion)
# GetArrayFromImage calls UpdateLargestPossibleRegion to ensure the image buffer
# has been populated
array = itk.GetArrayFromImage(extractor.GetOutput())
# GetArrayFromImage calls UpdateLargestPossibleRegion to ensure the image buffer
# has been populated with the correct region
extractionRegion.SetSize(10)
extractor.SetExtractionRegion(extractionRegion)
array = itk.GetArrayFromImage(extractor.GetOutput())
def generate_random_patches(image_list, h5_path, patch_size,
target_num_patches_each_label, valid_pct):
# printing process id
print('PID: {0}, number of samples: {1}'.format(os.getpid(),
len(image_list)))
for idx in range(image_list.shape[0]):
i_sample = image_list.iloc[idx]['ID']
print('PID: {0} -- '.format(os.getpid()),
image_list.iloc[idx]['image'][:])
# read image
itk_image = itk.imread(image_list.iloc[idx]['image'])
itk_annotation = itk.imread(image_list.iloc[idx]['label'])
np_image = itk.array_from_image(itk_image)
np_annotation = itk.array_from_image(itk_annotation)
#normalized
np_image = (np_image - np_image.mean()) / np_image.std()
threshold_gray_value = np_image.mean() - 2 * np_image.std()
# get valid range
valid_range = np.zeros([3, 2], dtype=np.int32)
valid_range[0, 1] = np_image.shape[0] - patch_size[0]
valid_range[1][1] = np_image.shape[1] - patch_size[1]
valid_range[2][1] = np_image.shape[2] - patch_size[2]
patch_image = np.zeros([
target_num_patches_each_label.sum(), 1, patch_size[0],
patch_size[1], patch_size[2]
]) #Batch x C x W x D x H
patch_label = np.zeros([
target_num_patches_each_label.sum(), 1, patch_size[0],
patch_size[1], patch_size[2]
]) #Batch x C x W x D x H
patch_volume = patch_size[0] * patch_size[1] * patch_size[2]
# randomly sampled
i_num_valid_patches = 0
visited_location = []
for i_label in range(target_num_patches_each_label.shape[0]):
i_num_valid_patches_each_label = 0
while i_num_valid_patches_each_label < target_num_patches_each_label[
i_label]:
k = randint(valid_range[0, 0], valid_range[0, 1])
j = randint(valid_range[1, 0], valid_range[1, 1])
i = randint(valid_range[2, 0], valid_range[2, 1])
i_location = [k, j, i] # bottom left corner, i.e., 000
if not i_location in visited_location:
i_patch_image = np_image[k:(k + patch_size[0]),
j:(j + patch_size[1]),
i:(i + patch_size[2])]
i_patch_label = np_annotation[k:(k + patch_size[0]),
j:(j + patch_size[1]),
i:(i + patch_size[2])]
if (np.sum(i_patch_label == i_label) >
patch_volume * valid_pct) and (np.sum(
i_patch_image > threshold_gray_value)):
visited_location.append(i_location) # valid visit
patch_image[i_num_valid_patches,
0, :, :, :] = i_patch_image
patch_label[i_num_valid_patches,
0, :, :, :] = i_patch_label
i_num_valid_patches_each_label += 1
i_num_valid_patches += 1
# print('Current total number of patches: {0}\n For label of {1}: patch No. {2}'.format(i_num_valid_patches, i_label, i_num_valid_patches_each_label))
# shuffle
randnum = list(range(patch_image.shape[0]))
np.random.shuffle(randnum)
patch_image = patch_image[randnum, :]
patch_label = patch_label[randnum, :]
# save to h5
if not os.path.exists(h5_path):
os.makedirs(h5_path)
for i_patch in range(patch_image.shape[0]):
patch_file_name = os.path.join(
h5_path, 'sample_{0}_patch_{1}x{2}x{3}_{4}.h5'.format(
i_sample, patch_size[0], patch_size[1], patch_size[2],
i_patch))
#check old patch file
if os.path.isfile(patch_file_name):
os.remove(patch_file_name)
#output h5 file
with h5py.File(patch_file_name, 'w') as f:
f['image'] = patch_image[i_patch, :, :, :, :]
f['label'] = patch_label[i_patch, :, :, :, :]
import itk
import faulthandler
faulthandler.enable()
fixed_image = itk.imread('data/CT_2D_head_fixed.mha', itk.F)
moving_image = itk.imread('data/CT_2D_head_moving.mha', itk.F)
parameter_object = itk.ParameterObject.New()
default_rigid_parameter_map = parameter_object.GetDefaultParameterMap('rigid')
parameter_object.AddParameterMap(default_rigid_parameter_map)
result_image, result_transform_parameters = itk.elastix_registration_method(
fixed_image, moving_image,
parameter_object=parameter_object,
log_to_console=False)
#
#==========================================================================*/
import itk
import sys
if len(sys.argv) < 4:
print('Usage: ' + sys.argv[0] +
' <MovingImage> <TransformParameters> <ResultImage>')
sys.exit(1)
moving_filename = sys.argv[1]
transform_parameters_filename = sys.argv[2]
result_filename = sys.argv[3]
moving = itk.imread(moving_filename, itk.F)
parameters = itk.ParameterObject.New()
parameters.ReadParameterFile(transform_parameters_filename)
print(parameters)
# Object oriented interface
ImageType = itk.Image[itk.F, 3]
transformix = itk.TransformixFilter[ImageType].New()
transformix.SetMovingImage(moving)
transformix.SetLogToConsole(True)
transformix.SetTransformParameterObject(parameters)
transformix.UpdateLargestPossibleRegion()
result = transformix.GetOutput()
itk.imwrite(result, result_filename)
def compute_dice_scores(modelresults_folder, resultsdir_3Dlabels, with3D,
ROI_list):
# create files to save the dice scores
dice_file_2D = f'{modelresults_folder}/segm_results/dicescore_2Dmodel.txt'
dicescores_header = ''
for ROI in ROI_list:
dicescores_header = dicescores_header + f"{ROI}\t"
dicescores_header = dicescores_header + "Mean\n"
#num_ROIs = len(ROI_list)
with open(dice_file_2D, 'w') as f:
f.write(dicescores_header)
meandice_2D = 0
num_patients = 0
if with3D:
dice_file_3D = f'{modelresults_folder}/segm_results/dicescore_3Dmodel.txt'
with open(dice_file_3D, 'w') as f:
f.write(dicescores_header)
meandice_3D = 0
# calculate dice between predicted and real labels for 2D and 3D
for reallabel in glob.glob(f'{resultsdir_3Dlabels}/segmap*.mha'):
print(reallabel)
num_patients = num_patients + 1
label_2Dmodel = reallabel.replace(
f'{resultsdir_3Dlabels}/segmap',
f'{modelresults_folder}/segm_results/merged_labels/label_patient')
reallabel_array = itk.GetArrayFromImage(itk.imread(reallabel))
label_2Dmodel_array = itk.GetArrayFromImage(itk.imread(label_2Dmodel))
dicescores_2D = ''
meandice_2D_perpatient = 0
if with3D:
label_3Dmodel = reallabel.replace(
f'{resultsdir_3Dlabels}/segmap',
f'{modelresults_folder}/OUTPUT_DIRECTORY_3D/CBCT_3D_').replace(
'.mha', '.nii.gz')
label_3Dmodel_array = itk.GetArrayFromImage(
itk.imread(label_3Dmodel))
dicescores_3D = ''
meandice_3D_perpatient = 0
num_labels_2D = 0
num_labels_3D = 0
for ROI_ind, ROI in enumerate(ROI_list):
# the class value = ROI_ind + 1
k = ROI_ind + 1
#print(np.sum(label_2Dmodel_array[reallabel_array==k]==k)*2.0 )
#print(np.sum(label_2Dmodel_array[label_2Dmodel_array==k]==k) + np.sum(reallabel_array[reallabel_array==k]==k))
dice_2D = np.sum(
label_2Dmodel_array[reallabel_array == k] == k) * 2.0 / (
np.sum(label_2Dmodel_array[label_2Dmodel_array == k] == k)
+ np.sum(reallabel_array[reallabel_array == k] == k))
if not math.isnan(dice_2D):
meandice_2D_perpatient = meandice_2D_perpatient + dice_2D
num_labels_2D = num_labels_2D + 1
dicescores_2D = dicescores_2D + f'{dice_2D}\t'
if with3D:
dice_3D = np.sum(
label_3Dmodel_array[reallabel_array == k] == k) * 2.0 / (
np.sum(
label_3Dmodel_array[label_3Dmodel_array == k] == k)
+ np.sum(reallabel_array[reallabel_array == k] == k))
dicescores_3D = dicescores_3D + f'{dice_3D}\t'
print(
np.sum(label_3Dmodel_array[reallabel_array == k] == k) *
2.0)
print(
np.sum(label_3Dmodel_array[label_3Dmodel_array == k] == k))
print(np.sum(reallabel_array[reallabel_array == k] == k))
print(dice_3D)
if not math.isnan(dice_3D):
meandice_3D_perpatient = meandice_3D_perpatient + dice_3D
num_labels_3D = num_labels_3D + 1
meandice_2D_perpatient = meandice_2D_perpatient / num_labels_2D
meandice_2D = meandice_2D + meandice_2D_perpatient
dicescores_2D = dicescores_2D + f"{meandice_2D_perpatient}\n"
with open(dice_file_2D, "a") as f:
f.write(dicescores_2D)
if with3D:
meandice_3D_perpatient = meandice_3D_perpatient / num_labels_3D
meandice_3D = meandice_3D + meandice_3D_perpatient
dicescores_3D = dicescores_3D + f"{meandice_3D_perpatient}\n"
with open(dice_file_3D, "a") as f:
f.write(dicescores_3D)
# calculate mean of means
with open(dice_file_2D, "a") as f:
f.write(f"Total mean: {meandice_2D/num_patients}\n")
if with3D:
with open(dice_file_3D, "a") as f:
f.write(f"Total mean: {meandice_3D/num_patients}\n")
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0.txt
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
#==========================================================================*/
import itk
import sys
if len(sys.argv) < 4:
print('Usage: ' + sys.argv[0] +
' <FixedImage> <MovingImage> <ResultImage>')
sys.exit(1)
fixed_filename = sys.argv[1]
moving_filename = sys.argv[2]
result_filename = sys.argv[3]
fixed = itk.imread(fixed_filename, itk.F)
moving = itk.imread(moving_filename, itk.F)
result = itk.elastix_registration_method(fixed, moving)
itk.imwrite(result, result_filename)
# distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # #==========================================================================*/ # # Example on the use of the LaplacianImageFilter # import itk from sys import argv itk.auto_progress(2) edges = itk.imread(argv[1], itk.F) houghF = itk.HoughTransform2DLinesImageFilter[itk.F, itk.F].New() houghF.SetInput(edges) houghF.SetAngleResolution(100) houghF.SetNumberOfLines(2) houghF.Update() detected_lines = houghF.GetLines() # Check that we detected 2 lines as we requested. assert len(detected_lines) == 2 # Check that we can access the line by index. line1 = detected_lines[0] # Check that we can access the points of the line assert len(line1.GetPoints()) == 2
#
#==========================================================================*/
#
# Test the performance of the BModeImageFilter using Python
#
import itk
from sys import argv
input_filename = argv[1]
output_filename = argv[2]
PixelType = itk.F
dim = 3
ImageType = itk.Image[PixelType, dim]
inputImg = itk.imread(input_filename, PixelType)
inputImg.DisconnectPipeline()
BModeFilterType = itk.BModeImageFilter[ImageType, ImageType]
bMode = BModeFilterType.New()
bMode.SetInput(inputImg)
WindowingType = itk.IntensityWindowingImageFilter[ImageType, ImageType]
window = WindowingType.New()
window.SetInput(bMode.GetOutput())
clock = itk.TimeProbe()
runs = 1000
for i in range(runs):
bMode.Modified()
print(error)
# Mask Creation and Location
directory3 = (studyname + '-MinMax/')
try:
os.mkdir(directory3)
except OSError as error:
print(error)
pic_folder = os.listdir(directory)
pic_folder = [pic_folder for pic_folder in pic_folder if ".nii" in pic_folder]
pic_folder.sort()
print(pic_folder)
num_images = len(pic_folder)
im0Tmp = itk.imread(directory + pic_folder[int(num_images / 2)], itk.F)
resample = ttk.ResampleImage.New(Input=im0Tmp, MakeIsotropic=True)
resample.Update()
im0 = resample.GetOutput()
immath = ttk.ImageMath.New(Input=im0)
immath.Blur(1)
im0Blur = immath.GetOutput()
immath.Threshold(150, 800, 1, 0)
immath.Dilate(10, 1, 0)
mask0 = immath.GetOutputUChar()
mask0Tmp = itk.GetArrayViewFromImage(mask0)
mask0Tmp[0:4, :, :] = 0
sizeZ = mask0Tmp.shape[0]
mask0Tmp[
# Copyright NumFOCUS
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0.txt
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import itk
parser = argparse.ArgumentParser(
description="{{ cookiecutter.example_title }}.")
parser.add_argument("input_image")
parser.add_argument("output_image")
args = parser.parse_args()
input_image = itk.imread(args.input_image)
output_image = itk.itkHelpers.camel_to_snake_case({{cookiecutter.class_name}
})(input_image)
itk.imwrite(output_image, args.output_image)
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0.txt
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
#==========================================================================*/
from __future__ import print_function
import itk
from sys import argv, stderr, exit
itk.auto_progress(2)
if (len(argv) < 2):
print((
"Missing Parameters \n Usage: IntensityWindowingImageFilter.py inputImageFile"
),
file=stderr)
exit(1)
image = itk.imread(argv[1], itk.F)
# Verifies that ITK supports getting tuples for filter parameters. Not testing the filter
# results.
intensity_filter = itk.IntensityWindowingImageFilter.New(
image, WindowLevel=[255, 127])
s = itk.region(reader.GetOutput())
assert s.GetIndex()[0] == s.GetIndex()[1] == 0
assert s.GetSize()[0] == s.GetSize()[1] == 256
# test range
assert itk.range(reader) == (0, 255)
assert itk.range(reader.GetOutput()) == (0, 255)
# test write
itk.imwrite(reader, sys.argv[2])
itk.imwrite(reader, sys.argv[2], True)
# test read
image=itk.imread(fileName)
assert type(image) == itk.Image[itk.RGBPixel[itk.UC],2]
image=itk.imread(fileName, itk.F)
assert type(image) == itk.Image[itk.F,2]
# test search
res = itk.search("Index")
assert res[0] == "Index"
assert res[1] == "index"
assert "ContinuousIndex" in res
res = itk.search("index", True)
assert "Index" not in res
# test down_cast
def convertPatientAPI(inputfolder, ipp):
#Create output directory based on ipp research
patientParentDirectory = os.path.dirname(inputfolder)
id = inputfolder[-7:]
outputDirectory = os.path.join(patientParentDirectory, "output", "patient." + str(ipp))
if os.path.isdir(outputDirectory):
print(outputDirectory + " already exists")
return(0)
os.makedirs(outputDirectory)
os.makedirs(os.path.join(outputDirectory, "input"))
#Find all studies irradiation in the patient folder
studies = {}
inputRTDose = []
inputRTStruct = []
for root, dirs, files in os.walk(inputfolder):
for dir in dirs:
if root.endswith("CT_SET"):
studies[dir] = {}
studies[dir]["inputCT"] = []
studies[dir]["inputCBCT"] = []
studies[dir]["inputRTStruct"] = ""
studies[dir]["inputPlan"] = ""
studies[dir]["inputINI"] = ""
os.makedirs(os.path.join(outputDirectory, "input", dir))
#Find all input images
for root, dirs, files in os.walk(inputfolder):
for file in files:
if file.startswith("CT_IMAGE_") and file.endswith(".DCM"):
dir = os.path.basename(root)
studies[dir]["inputCT"] += [os.path.join(root, file)]
if file.startswith("DCMTPS_Calculated") and file.endswith(".dcm"):
inputRTDose += [os.path.join(root, file)]
if root.endswith("CT_SET") and file.endswith(".DCM"):
inputRTStruct += [os.path.join(root, file)]
if root.endswith("DICOM_PLAN") and file.endswith(".DCM"):
ds = pydicom.read_file(os.path.join(root, file))
dir = ds[(0x0008, 0x0018)].value
if dir in studies.keys():
studies[dir]["inputPlan"] = os.path.join(root, file)
struct = ds[(0x300c,0x0060)][0][(0x0008,0x1155)].value
studies[dir]["inputRTStruct"] = struct
if root.endswith("Reconstruction") and file.endswith(".SCAN"):
year = file.split(".")[-2][:4]
month = file.split(".")[-3]
day = file.split(".")[-4]
hour = file.split(".")[-2][4:6]
minute = file.split(".")[-2][6:8]
second = file.split(".")[-2][8:10]
if os.path.isfile(os.path.join(root, file[:-4] + "INI")):
bashCommand = "cat " + os.path.join(root, file[:-4] + "INI")
process = subprocess.Popen(bashCommand.split(), stdout=subprocess.PIPE)
output, error = process.communicate()
output = output.split(b'\r\n')
for element in output:
if element.startswith(b"ReferenceUID="):
dir = element.split(b'=')[1].decode("utf-8")
studies[dir]["inputCBCT"] += [(os.path.join(root, file), str(year)+str(month)+str(day)+str(hour)+str(minute)+str(second))]
if "CT_SET" in root and file.startswith("1.") and file.endswith(".INI"):
dir = os.path.basename(root)
studies[dir]["inputINI"] = os.path.join(root, file)
for struct in inputRTStruct:
ds = pydicom.read_file(struct)
structId = ds[(0x0008, 0x0018)].value
for study in studies.keys():
if structId == studies[study]["inputRTStruct"]:
studies[study]["inputRTStruct"] = struct
for dir in studies.keys():
studies[dir]["inputCT"].sort(key=natural_keys)
studies[dir]["inputCBCT"].sort(key=natural_keys_second)
#Remove wrong cbct (if 2 cbct are done the same day, keep the second -> it means that the first has a wrong alignment)
removeIndexCBCT = []
for indexCBCT in range(1, len(studies[dir]["inputCBCT"])):
if int(studies[dir]["inputCBCT"][indexCBCT][1]) - int(studies[dir]["inputCBCT"][indexCBCT-1][1]) < 10000:
removeIndexCBCT += [indexCBCT-1]
studies[dir]["inputCBCT"] = [i for j, i in enumerate(studies[dir]["inputCBCT"]) if j not in removeIndexCBCT]
#Convert CT
inputDicomCT = gt.read_dicom(studies[dir]["inputCT"])
#outputCT = gt.image_convert(inputDicomCT)
itk.imwrite(inputDicomCT, os.path.join(outputDirectory, "input", dir, "CT.nii"))
transfoCT = np.array([[1.,0,0,0], [0,1.,0,0], [0,0,1.,0], [0,0,0,1.]])
#Convert CBCT threw clitk
cbctIndex = 0
for cbct in studies[dir]["inputCBCT"]:
if os.path.isfile(cbct[0][:-4] + "INI.XVI"):
#Read CBCT transformation matrix
with open(cbct[0][:-4] + "INI.XVI") as f:
for line in f.readlines():
if "OnlineToRefTransformCorrection=" in line:
outputCBCT = os.path.join(outputDirectory, "input", dir, "cbct." + str(cbctIndex) + ".nii")
tempLine = line.split('=')[1]
if tempLine[0] == " ":
tempLine = tempLine[1:]
transfoMatrix = np.array([float(t) for t in tempLine.split()]).reshape(4,4).T
transfoMatrix[:-1, -1] *= 10 #convert from cm to mm
transfoMatCT2CBCT = transfoCT.dot(transfoMatrix)
transfoMatCBCT2CT = invertTransfoMat(transfoMatCT2CBCT)
np.savetxt(outputCBCT[:-3] + "mat", transfoMatCBCT2CT)
bashCommand = "clitkImageConvert -i " + cbct[0] + " -o " + outputCBCT
process = subprocess.Popen(bashCommand.split(), stdout=subprocess.PIPE)
output, error = process.communicate()
bashCommand = "clitkAffineTransform -i " + outputCBCT + " -o " + outputCBCT + " -m " + outputCBCT[:-3] + "mat" + " --pad=-1024 --transform_grid"
process = subprocess.Popen(bashCommand.split(), stdout=subprocess.PIPE)
output, error = process.communicate()
os.remove(outputCBCT[:-3] + "mat")
break
cbctIndex += 1
#Convert struct threw clitk and after to .nii
inputTmpRTStruct = []
if not studies[dir]["inputRTStruct"] == "":
bashCommand = "clitkDicomRTStruct2Image -c --mha -i " + studies[dir]["inputRTStruct"] + " -o " + os.path.join(outputDirectory, "input", dir, "tmp.rtstruct.") + " -j " + os.path.join(outputDirectory, "input", dir, "CT.nii")
process = subprocess.Popen(bashCommand.split(), stdout=subprocess.PIPE)
output, error = process.communicate()
for root, dirs, files in os.walk(os.path.join(outputDirectory, "input", dir)):
for file in files:
if file.startswith("tmp.rtstruct.") and file.endswith(".mha"):
if not file == file.encode('utf-8', 'replace').decode():
continue
inputTmpRTStruct += [file]
structName = []
for struct in inputTmpRTStruct:
inputStruct = itk.imread(os.path.join(outputDirectory, "input", dir, struct))
itk.imwrite(inputStruct, os.path.join(outputDirectory, "input", dir, struct[13:-3] + "nii"))
structName += [struct[13:-4]]
os.remove(os.path.join(outputDirectory, "input", dir, struct))
#Anonymise rtstruct
if not studies[dir]["inputRTStruct"] == "":
anonymizeDicomFile(studies[dir]["inputRTStruct"], os.path.join(outputDirectory, "input", dir, "rtstruct.dcm"), "anonymous", ipp)
#Anonymise dicom plan
if not studies[dir]["inputPlan"] == "":
anonymizeDicomFile(studies[dir]["inputPlan"], os.path.join(outputDirectory, "input", dir, "rtplan.dcm"), "anonymous", ipp)
#Anonymise rt dose
indexDose = 0
for dose in inputRTDose:
anonymizeDicomFile(dose, os.path.join(outputDirectory, "input", dir, "rtdose" + str(indexDose) + ".dcm"), "anonymous", ipp)
indexDose += 1
#Keep treatment from .INI file
bashCommand = "cat " + studies[dir]["inputINI"]
process = subprocess.Popen(bashCommand.split(), stdout=subprocess.PIPE)
output, error = process.communicate()
output = output.split(b'\r\n')
for element in output:
if element.startswith(b"TreatmentID="):
treatmentName = element.split(b'=')[1].decode("utf-8")
#Write id and ipp in json file
try:
jsonData = load('delpel.json')
except:
jsonData = {}
jsonData['patients'] = {}
jsonData['header'] = {
"key": "ipp research",
"treatment id": "TreatmentID value found in .INI file",
"clarity": "Does the patient treated with Clarity (artifacts)",
"machine": "Name of the treatment machine",
"city": "Treatment city",
"complete bladder": "True if the bladder mask is not truncated",
"ROI name in rtStruct": "found corresponding name"
}
if not ipp in jsonData['patients']:
jsonData['patients'][ipp] = {}
if not dir in jsonData['patients'][ipp]:
jsonData['patients'][ipp][dir] = {
'treatment id': treatmentName,
'ReferenceUID': dir,
'Structures': {},
'clarity': False,
'city': "Lyon",
'complete bladder': True,
'machine': os.path.basename(os.path.dirname(inputfolder))
}
for struct in structName:
jsonData['patients'][ipp][dir]['Structures'][struct] = identifyStruct("_".join(struct.split("_")[1:]).upper())
with open("delpel.json", "w") as jsonFile:
json.dump(jsonData, jsonFile, indent=4, sort_keys=True)
