def VisualizeReferenceSpectrum(rf_files, freq_sampling):
plt.figure(1, figsize=(5, 4))
handles = []
labels = []
for rf_file in rf_files:
ComponentType = itk.ctype('float')
Dimension = 2
ImageType = itk.VectorImage[ComponentType, Dimension]
reader = itk.ImageFileReader[ImageType].New(FileName=rf_file)
reader.Update()
image = reader.GetOutput()
arr = itk.GetArrayFromImage(image)
arr /= arr[:,:,arr.shape[2]/3-arr.shape[2]/5:arr.shape[2]/2+arr.shape[2]/5].max()
freq = np.linspace(freq_sampling / 2 / arr.shape[2], freq_sampling / 2, arr.shape[2])
ax = plt.plot(freq, arr[0, 0, :].ravel(), label=rf_file)
handles.append(ax[0])
labels.append(rf_file)
plt.xlabel('Frequency [Hz]')
plt.ylabel('Power spectrum [V]')
plt.figlegend(handles, labels, 'upper right')
plt.ylim(0.0, 1.0)
dirname = os.path.dirname(rf_files[0])
plt.savefig(os.path.join(dirname, 'ReferenceSpectrum.png'), dpi=300)
plt.show()
def Resample_Labels(labels_filepath, reference_path, output_path):
PixelType = itk.ctype('unsigned char')
Dimension = 2
ImageType = itk.Image[PixelType, Dimension]
basename = os.path.basename(labels_filepath).split('_ManualLabel')[0]
labels_reader = itk.ImageFileReader[ImageType].New()
labels_reader.SetFileName(labels_filepath)
labels_reader.UpdateLargestPossibleRegion()
reference_filepath_glob = os.path.join(os.path.dirname(labels_filepath),
reference_path, basename + '*')
reference_filepath = glob.glob(reference_filepath_glob)[0]
reference_reader = itk.ImageFileReader[ImageType].New()
reference_reader.SetFileName(reference_filepath)
reference_reader.UpdateLargestPossibleRegion()
resampler = itk.ResampleImageFilter[ImageType, ImageType].New()
resampler.SetInput(labels_reader.GetOutput())
resampler.SetReferenceImage(reference_reader.GetOutput())
resampler.SetUseReferenceImage(True)
resampler.UpdateLargestPossibleRegion()
output_path = os.path.join(os.path.dirname(labels_filepath), output_path)
if not os.path.exists(output_path):
os.makedirs(output_path)
writer = itk.ImageFileWriter.New(resampler.GetOutput())
writer.SetFileName(os.path.join(output_path, basename + '_ManualLabelResampled.mha'))
writer.Update()
def test_mesh_to_geometry():
# 3D
Dimension = 3
PixelType = itk.ctype('double')
MeshType = itk.Mesh[PixelType, Dimension]
mesh = MeshType.New()
PointType = itk.Point[itk.F, Dimension]
point0 = PointType()
point0[0] = -1
point0[1] = -1
point0[2] = 0
mesh.SetPoint(0, point0)
point1 = PointType()
point1[0] = 1
point1[1] = -1
point1[2] = 0
mesh.SetPoint(1, point1)
point2 = PointType()
point2[0] = 1
point2[1] = 1
point2[2] = 0
mesh.SetPoint(2, point2)
point3 = PointType()
point3[0] = 1
point3[1] = 1
point3[2] = 0
mesh.SetPoint(3, point3)
geometry = to_geometry(mesh)
points = mesh.GetPoints()
point_template = itk.template(points)
element_type = point_template[1][1]
point_values = itk.PyVectorContainer[
element_type].array_from_vector_container(points)
assert (geometry['vtkClass'] == 'vtkPolyData')
assert (geometry['points']['vtkClass'] == 'vtkPoints')
assert (geometry['points']['numberOfComponents'] == 3)
assert (geometry['points']['dataType'] == 'Float32Array')
assert (geometry['points']['size'] == 4 * 3)
assert (np.array_equal(geometry['points']['values'],
point_values.astype(np.float32)))
def GetImage(img_np):
img_np_shape = np.shape(img_np)
ComponentType = itk.ctype('float')
Dimension = img_np.ndim - 1
PixelDimension = img_np.shape[-1]
print("Dimension:", Dimension, "PixelDimension:", PixelDimension)
if Dimension == 1:
OutputImageType = itk.VectorImage[ComponentType, 2]
else:
OutputImageType = itk.VectorImage[ComponentType, Dimension]
out_img = OutputImageType.New()
out_img.SetNumberOfComponentsPerPixel(PixelDimension)
size = itk.Size[OutputImageType.GetImageDimension()]()
size.Fill(1)
prediction_shape = list(img_np.shape[0:-1])
prediction_shape.reverse()
if Dimension == 1:
size[1] = prediction_shape[0]
else:
for i, s in enumerate(prediction_shape):
size[i] = s
index = itk.Index[OutputImageType.GetImageDimension()]()
index.Fill(0)
RegionType = itk.ImageRegion[OutputImageType.GetImageDimension()]
region = RegionType()
region.SetIndex(index)
region.SetSize(size)
out_img.SetRegions(region)
out_img.Allocate()
out_img_np = itk.GetArrayViewFromImage(out_img)
out_img_np.setfield(img_np.reshape(out_img_np.shape), out_img_np.dtype)
return out_img
def readDICOMImage(dir, outPutDir, identifier):
PixelType = itk.ctype('signed short')
Dimension = 3
ImageType = itk.Image[PixelType, Dimension]
namesGenerator = itk.GDCMSeriesFileNames.New()
namesGenerator.SetUseSeriesDetails(False)
namesGenerator.SetGlobalWarningDisplay(False)
namesGenerator.SetDirectory(dir)
seriesUID = namesGenerator.GetSeriesUIDs()
if len(seriesUID) < 1:
print('ERROR: No DICOMS in folder' + dir)
sys.exit(1)
if len(seriesUID) > 1:
print('ERROR: In folder are more series')
sys.exit(1)
print('DICOMs red OK from: ' + dir)
# SHOULD BE just one
for uid in seriesUID:
seriesIdentifier = uid
print('Reading: ' + seriesIdentifier)
fileNames = namesGenerator.GetFileNames(seriesIdentifier)
reader = itk.ImageSeriesReader[ImageType].New()
dicomIO = itk.GDCMImageIO.New()
reader.SetImageIO(dicomIO)
reader.SetFileNames(fileNames)
outFileName = 'DIC_' + identifier + '.nrrd'
writer = itk.ImageFileWriter[ImageType].New()
writer.SetFileName(outPutDir + '/' + outFileName)
writer.UseCompressionOn()
writer.SetInput(reader.GetOutput())
print('Writing: ' + outPutDir + '/' + outFileName)
writer.Update()
return sitk.ReadImage(outPutDir + '/' + outFileName, sitk.sitkFloat32)
def laplacian_recursive_gaussian_filter(image):
'''
reference: C++ version https://itk.org/ITKExamples/src/Filtering/ImageFeature/LaplacianRecursiveGaussianImageFilter/Documentation.html
:param image: itk 3D image
:return: itk 3D image
'''
input_pixel_type = itk.ctype('float')
input_image_type = itk.Image[input_pixel_type, 3]
filter = itk.LaplacianRecursiveGaussianImageFilter[input_image_type,
input_image_type].New()
filter.SetInput(image)
filter.Update()
result = filter.GetOutput()
# rescale_filter = itk.RescaleIntensityImageFilter[input_image_type,input_image_type].New()
# rescale_filter.SetInput(result)
# outputPixelTypeMinimum = itk.NumericTraits[input_image_type].min()
# outputPixelTypeMaximum = itk.NumericTraits[input_image_type].max()
#
#
return result
def read_dicom_series(patient_dir):
'''
given a image dir
return itk image
:param patient_dir:
:return:
'''
input_pixel_type = itk.ctype('float')
image_type = itk.Image[input_pixel_type, 3]
reader = itk.ImageSeriesReader[image_type].New()
gdcm_io = itk.GDCMImageIO.New()
reader.SetImageIO(gdcm_io)
names_generator = itk.GDCMSeriesFileNames.New()
names_generator.SetInputDirectory(patient_dir)
file_names = names_generator.GetInputFileNames()
reader.SetFileNames(file_names)
try:
reader.Update()
except:
raise
return reader.GetOutput()
def create_itk_image(dimension, pixel_string, image_region):
'''Create an ITK image.
No checks are performed to guarantee the inputs are valid
inputs.
Args:
dimension (int): Image dimensions
pixel_string (string): The pixel type as a string
image_region (itk.ImageRegion): The image region over which the image is defined
Returns:
itk.Image: The created ITK image
'''
pixel_type = itk.ctype(pixel_string)
image_type = itk.Image[pixel_type, dimension]
image = image_type.New()
image.SetRegions(image_region)
image.Allocate()
return image
def test_create_3d_image_successful(self):
'''Test that create_itk_image can create a 3D image.'''
# Parameters
dimension = 3
pixel_string = 'unsigned char'
pixel_type = itk.ctype(pixel_string)
image_type = itk.Image[pixel_type, dimension]
this_image = image_type.New()
start = itk.Index[dimension]()
start[0] = 0 # first index on X
start[1] = 0 # first index on Y
start[2] = 0 # first index on Z
size = itk.Size[dimension]()
size[0] = 200 # size along X
size[1] = 200 # size along Y
size[2] = 200 # size along Z
image_region = itk.ImageRegion[dimension]()
image_region.SetSize(size)
image_region.SetIndex(start)
this_image.SetRegions(image_region)
this_image.Allocate()
# Call method
created_image = create_itk_image(dimension, pixel_string, image_region)
# Need to compare the components
self.assertEqual(this_image.GetImageDimension(),
created_image.GetImageDimension())
self.assertEqual(this_image.GetDirection(),
created_image.GetDirection())
self.assertEqual(this_image.GetLargestPossibleRegion(), \
created_image.GetLargestPossibleRegion())
self.assertEqual(this_image.GetOrigin(), created_image.GetOrigin())
self.assertEqual(this_image.GetSpacing(), created_image.GetSpacing())
def image_convert(inputImage, pixeltype=None):
"""
Compute relative statistical uncertainty
- inputImage: input itk image to convert
- pixeltype: string representing the type of the output pixel:
- unsigned char
- signed short
- unsigned short
- float
"""
#If pixel type is not None, convert it,
#If None it could be a type conversion without pixel conversion
if pixeltype is not None:
InputType = type(inputImage)
OutputType = itk.Image[itk.ctype(pixeltype),
inputImage.GetImageDimension()]
castFilter = itk.CastImageFilter[InputType, OutputType].New()
castFilter.SetInput(inputImage)
return castFilter
return inputImage
def VisualizePixelSpectrum(spectra_file, freq_sampling, index):
plt.figure(1, figsize=(5, 4))
handles = []
labels = []
ComponentType = itk.ctype('float')
Dimension = 2
ImageType = itk.VectorImage[ComponentType, Dimension]
reader = itk.ImageFileReader[ImageType].New(FileName=spectra_file)
reader.Update()
image = reader.GetOutput()
arr = itk.GetArrayFromImage(image)
freq = np.linspace(freq_sampling / 2 / arr.shape[2], freq_sampling / 2, arr.shape[2])
ax = plt.plot(freq, arr[index[1], index[0], :].ravel(), label=spectra_file)
handles.append(ax[0])
labels.append(spectra_file)
plt.xlabel('Frequency [Hz]')
plt.ylabel('Power spectral density')
plt.figlegend(handles, labels, 'upper right')
plt.ylim(0.0, 2.0)
dirname = os.path.dirname(spectra_file)
# plt.savefig(os.path.join(dirname, 'PixelSpectrum.png'), dpi=300)
plt.show()
def canny_filter(image, variance, lower_threshold, upper_threshold):
'''
reference: C++ version https://itk.org/ITKExamples/src/Filtering/ImageFeature/SobelEdgeDetectionImageFilter/Documentation.html
This filter is an implementation of a Canny edge detector for scalar-valued images.
:param image: itk 3D image
:param variance:Variance are used in the Gaussian smoothing
:param lower_threshold:lower_threshold is the lowest allowed value in the output image.
:param upper_threshold: values below the Threshold level will be replaced with the OutsideValue parameter value, whose default is zero.
:return:itk 3D image
'''
input_pixel_type = itk.ctype('float')
input_image_type = itk.Image[input_pixel_type, 3]
filter = itk.CannyEdgeDetectionImageFilter[input_image_type,
input_image_type].New()
filter.SetInput(image)
filter.SetVariance(variance)
filter.SetLowerThreshold(lower_threshold)
filter.SetUpperThreshold(upper_threshold)
filter.Update()
result = filter.GetOutput()
return result
def setUp(self):
'''Set up for TestGetITKImageType'''
# Create a temporary directory
self.test_dir = tempfile.mkdtemp()
# Create an image with a known orientation
pixel_types_as_strings = ['short', 'unsigned short', 'unsigned char']
dimensions = [2, 3]
extensions = ['nii', 'nii.gz', 'mhd', 'nrrd']
# Create a bunch of files
self.image_type_map = {}
self.file_names = []
for pixel_type_as_string in pixel_types_as_strings:
for dimension in dimensions:
# Create image
index = [0 for x in range(dimension)]
size = [10 for x in range(dimension)]
image_region = create_itk_image_region(dimension, index, size)
image = create_itk_image(dimension, pixel_type_as_string,
image_region)
image_type = itk.Image[itk.ctype(pixel_type_as_string),
dimension]
for extension in extensions:
# Create file name
file_name = os.path.join(
self.test_dir,
'image_{}_{}.{}'.format(pixel_type_as_string,
dimension, extension))
self.file_names.append(file_name)
self.image_type_map[file_name] = image_type
# Write image out
itk.imwrite(image, file_name)
def test_NumPyBridge_FortranOrder(self):
"Try to convert an ITK image to / from a NumPy array with Fortran order"
Dimension = 2
PixelType = itk.ctype('signed short')
ImageType = itk.Image[PixelType, Dimension]
dtype = np.int16
arr = np.arange(6, dtype=dtype)
arrC = np.reshape(arr, (2, 3), order='C')
assert(arrC.flags.c_contiguous)
image = itk.PyBuffer[ImageType].GetImageViewFromArray(arrC)
index = itk.Index[Dimension]()
index[0] = 0
index[1] = 0
assert(image.GetPixel(index) == 0)
index[0] = 1
index[1] = 0
assert(image.GetPixel(index) == 1)
index[0] = 0
index[1] = 1
assert(image.GetPixel(index) == 3)
arrFortran = np.reshape(arr, (3, 2), order='F')
assert(arrFortran.flags.f_contiguous)
image = itk.PyBuffer[ImageType].GetImageViewFromArray(arrFortran)
index[0] = 0
index[1] = 0
assert(image.GetPixel(index) == 0)
index[0] = 1
index[1] = 0
assert(image.GetPixel(index) == 1)
index[0] = 0
index[1] = 1
assert(image.GetPixel(index) == 3)
def test_NumPyBridge_FortranOrder(self):
"Try to convert an ITK image to / from a NumPy array with Fortran order"
Dimension = 2
PixelType = itk.ctype('signed short')
ImageType = itk.Image[PixelType, Dimension]
dtype = np.int16
arr = np.arange(6, dtype=dtype)
arrC = np.reshape(arr, (2, 3), order='C')
assert (arrC.flags.c_contiguous)
image = itk.PyBuffer[ImageType].GetImageViewFromArray(arrC)
index = itk.Index[Dimension]()
index[0] = 0
index[1] = 0
assert (image.GetPixel(index) == 0)
index[0] = 1
index[1] = 0
assert (image.GetPixel(index) == 1)
index[0] = 0
index[1] = 1
assert (image.GetPixel(index) == 3)
arrFortran = np.reshape(arr, (3, 2), order='F')
assert (arrFortran.flags.f_contiguous)
image = itk.PyBuffer[ImageType].GetImageViewFromArray(arrFortran)
index[0] = 0
index[1] = 0
assert (image.GetPixel(index) == 0)
index[0] = 1
index[1] = 0
assert (image.GetPixel(index) == 1)
index[0] = 0
index[1] = 1
assert (image.GetPixel(index) == 3)
# 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 distutils.version import StrictVersion as VS
if VS(itk.Version.GetITKVersion()) < VS("4.9.0"):
print("ITK 4.9.0 is required.")
sys.exit(1)
parser = argparse.ArgumentParser(
description="Perform 2D Translation Registration With Mean Squares.")
parser.add_argument("fixed_input_image")
parser.add_argument("moving_input_image")
parser.add_argument("output_image")
parser.add_argument("difference_image_after")
parser.add_argument("difference_image_before")
args = parser.parse_args()
PixelType = itk.ctype("float")
fixedImage = itk.imread(args.fixed_input_image, PixelType)
movingImage = itk.imread(args.moving_input_image, 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,
# 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
Dimension = 3
ComponentType = itk.ctype('float')
PixelType = itk.Vector[ComponentType, Dimension]
ImageType = itk.Image[PixelType, Dimension]
image = ImageType.New()
start = itk.Index[Dimension]()
start[0] = 0
start[1] = 0
start[2] = 0
size = itk.Size[Dimension]()
size[0] = 200
size[1] = 200
size[2] = 200
# 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 sys
import itk
if len(sys.argv) != 4:
print('Usage: ' + sys.argv[0] +
' input3DImageFile output3DImageFile sliceNumber')
sys.exit(1)
inputFilename = sys.argv[1]
outputFilename = sys.argv[2]
Dimension = 3
PixelType = itk.ctype('short')
ImageType = itk.Image[PixelType, Dimension]
reader = itk.ImageFileReader[ImageType].New()
reader.SetFileName(inputFilename)
reader.Update()
inputImage = reader.GetOutput()
extractFilter = itk.ExtractImageFilter.New(inputImage)
extractFilter.SetDirectionCollapseToSubmatrix()
# set up the extraction region [one slice]
inputRegion = inputImage.GetBufferedRegion()
size = inputRegion.GetSize()
size[2] = 1 # we extract along z direction
start = inputRegion.GetIndex()
def MotionCorrect(fixedImageFile,movingImageFile):
PixelType = itk.ctype('float')
fixedImage = itk.GetImageViewFromArray(fixedImageFile)
movingImage = itk.GetImageViewFromArray(movingImageFile)
# 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)
movingInitialTransform = TransformType.New()
initialParameters = movingInitialTransform.GetParameters()
initialParameters[0] = 0
initialParameters[1] = 0
movingInitialTransform.SetParameters(initialParameters)
registration.SetMovingInitialTransform(movingInitialTransform)
identityTransform = TransformType.New()
identityTransform.SetIdentity()
registration.SetFixedInitialTransform(identityTransform)
registration.SetNumberOfLevels(1)
registration.SetSmoothingSigmasPerLevel([0])
registration.SetShrinkFactorsPerLevel([1])
registration.Update()
# transform = registration.GetTransform()
# finalParameters = transform.GetParameters()
# translationAlongX = finalParameters.GetElement(0)
# translationAlongY = finalParameters.GetElement(1)
#
# numberOfIterations = optimizer.GetCurrentIteration()
#
# bestValue = optimizer.GetValue()
# print("Result = ")
# print(" Translation X = " + str(translationAlongX))
# print(" Translation Y = " + str(translationAlongY))
# print(" Iterations = " + str(numberOfIterations))
# print(" Metric value = " + str(bestValue))
CompositeTransformType = itk.CompositeTransform[itk.D, Dimension]
outputCompositeTransform = CompositeTransformType.New()
outputCompositeTransform.AddTransform(movingInitialTransform)
outputCompositeTransform.AddTransform(registration.GetModifiableTransform())
resampler = itk.ResampleImageFilter.New(Input=movingImage,
Transform=outputCompositeTransform,
UseReferenceImage=True,
ReferenceImage=fixedImage)
resampler.SetDefaultPixelValue(100)
OutputImageType = itk.Image[PixelType, Dimension]
caster = itk.CastImageFilter[FixedImageType,
OutputImageType].New(Input=resampler)
outputImageFile = itk.GetArrayFromImage(caster)
# writer = itk.ImageFileWriter.New(Input=caster, FileName=outputImageFile)
# writer.SetFileName(outputImageFile)
# writer.Update()
return outputImageFile
def Estimate_USRF_ReferenceSpectrum(input_filepath, side_lines=5, fft1D_size=128,
subregion_depth_fraction=1.0):
InputPixelType = itk.ctype('float')
Dimension = 2
ImageType = itk.Image[InputPixelType, Dimension]
reader = itk.ImageFileReader[ImageType].New()
reader.SetFileName(input_filepath)
input_RF = reader.GetOutput()
# Apply missing spacing information
if 'LinearProbe' in input_filepath:
change_information = itk.ChangeInformationImageFilter.New(input_RF)
change_information.SetUseReferenceImage(True)
change_information.SetChangeSpacing(True)
output_spacing = [1.0, 1.0]
size = itk.size(input_RF)
sos = 1540.
fs = 30000.
output_spacing[0] = sos / (2 * fs)
transducer_width = 30.0
output_spacing[1] = transducer_width / (size[1] - 1)
change_information.SetOutputSpacing(output_spacing)
input_RF = change_information.GetOutput()
input_RF.UpdateOutputInformation()
# Look for the peak signal amplitude in a sub-region
subregion_filter = itk.RegionOfInterestImageFilter.New(input_RF)
subregion = itk.region(input_RF)
input_size = itk.size(input_RF)
input_index = itk.index(input_RF)
# Skip the initial 10% of the beamline, which is in the nearfield
subregion_index = itk.Index[Dimension]()
subregion_index[0] = int(input_size[0] * 0.1)
subregion_index[1] = input_index[1]
subregion.SetIndex(subregion_index)
subregion_size = itk.Size[Dimension]()
subregion_size[0] = input_size[0] - subregion_index[0]
subregion_size[0] = subregion_size[0] - int((1. - subregion_depth_fraction) * subregion_size[0])
subregion_size[1] = input_size[1]
subregion.SetSize(subregion_size)
subregion_filter.SetRegionOfInterest(subregion)
subregion_filter.UpdateLargestPossibleRegion()
subregion_image = subregion_filter.GetOutput()
max_calculator = itk.MinimumMaximumImageCalculator.New(subregion_image)
max_calculator.ComputeMaximum()
max_index = max_calculator.GetIndexOfMaximum()
SideLinesPixelType = itk.ctype('unsigned char')
SideLinesImageType = itk.Image[SideLinesPixelType, Dimension]
side_lines_image = SideLinesImageType.New()
side_lines_image.CopyInformation(subregion_image)
side_lines_image.SetRegions(subregion_image.GetLargestPossibleRegion())
side_lines_image.Allocate()
side_lines_image.FillBuffer(side_lines)
spectra_window_filter = itk.Spectra1DSupportWindowImageFilter.New(side_lines_image)
spectra_window_filter.SetFFT1DSize(fft1D_size)
spectra_filter = itk.Spectra1DImageFilter.New(subregion_filter)
spectra_filter.SetSupportWindowImage(spectra_window_filter.GetOutput())
spectra_filter.UpdateLargestPossibleRegion()
reference_spectrum_filter = itk.RegionOfInterestImageFilter.New(spectra_filter)
reference_spectrum_region = itk.region(spectra_filter.GetOutput())
reference_spectrum_region.SetIndex(max_index)
reference_spectrum_size = itk.Size[Dimension]()
reference_spectrum_size.Fill(1)
reference_spectrum_region.SetSize(reference_spectrum_size)
reference_spectrum_filter.SetRegionOfInterest(reference_spectrum_region)
input_dir, input_file = os.path.split(input_filepath)
output_dir = os.path.join(input_dir, 'ReferenceSpectrum')
if not os.path.exists(output_dir):
os.makedirs(output_dir)
input_filename, input_fileext = os.path.splitext(input_file)
identifier = '_ReferenceSpectrum_side_lines_{:02d}_fft1d_size_{:03d}.mha'.format(side_lines, fft1D_size)
output_file = os.path.join(output_dir, input_filename + identifier)
writer = itk.ImageFileWriter.New(reference_spectrum_filter)
writer.SetFileName(output_file)
writer.SetUseCompression(True)
writer.Update()
# since higher values quickly start to undersegment the image.
import itk
import argparse
parser = argparse.ArgumentParser(
description="Segment With Watershed Image Filter.")
parser.add_argument("input_image")
parser.add_argument("output_image")
parser.add_argument("threshold", type=float)
parser.add_argument("level", type=float)
args = parser.parse_args()
Dimension = 2
FloatPixelType = itk.ctype("float")
FloatImageType = itk.Image[FloatPixelType, Dimension]
reader = itk.ImageFileReader[FloatImageType].New()
reader.SetFileName(args.input_image)
gradientMagnitude = itk.GradientMagnitudeImageFilter.New(
Input=reader.GetOutput())
watershed = itk.WatershedImageFilter.New(Input=gradientMagnitude.GetOutput())
threshold = args.threshold
level = args.level
watershed.SetThreshold(threshold)
watershed.SetLevel(level)
def vtk_cast_map():
"""Set up mapping between VTK array numeric types to VTK numeric types
that correspond to supported ITK numeric ctypes supported by the ITK
wrapping."""
global _vtk_cast_types
if _vtk_cast_types is None:
import itk
_vtk_cast_types = {}
# Map from VTK array type to list of possible types to which they can be
# converted, listed in order of preference based on representability
# and memory size required.
import vtk
type_map = {
vtk.VTK_UNSIGNED_CHAR: [
'unsigned char',
'unsigned short',
'unsigned int',
'unsigned long',
'signed short',
'signed int',
'signed long',
'float',
'double'
],
vtk.VTK_CHAR: [
'signed char',
'signed short',
'signed int',
'signed long',
'float',
'double'
],
vtk.VTK_SIGNED_CHAR: [
'signed char',
'signed short',
'signed int',
'signed long',
'float',
'double'
],
vtk.VTK_UNSIGNED_SHORT: [
'unsigned short',
'unsigned int',
'unsigned long',
'signed int',
'signed long',
'float',
'double'
],
vtk.VTK_SHORT: [
'signed short',
'signed int',
'signed long',
'float',
'double'
],
vtk.VTK_UNSIGNED_INT: [
'unsigned int',
'unsigned long',
'signed long',
'float',
'double'
],
vtk.VTK_INT: [
'signed int',
'signed long',
'float',
'double'
],
vtk.VTK_FLOAT: [
'float',
'double'
],
vtk.VTK_DOUBLE: [
'double',
'float'
]
}
# Map ITK ctype back to VTK type
ctype_to_vtk = {
'unsigned char': vtk.VTK_UNSIGNED_CHAR,
'signed char': vtk.VTK_CHAR,
'unsigned short': vtk.VTK_UNSIGNED_SHORT,
'signed short': vtk.VTK_SHORT,
'unsigned int': vtk.VTK_UNSIGNED_INT,
'signed int': vtk.VTK_INT,
'unsigned long': vtk.VTK_UNSIGNED_LONG,
'signed long': vtk.VTK_LONG,
'float': vtk.VTK_FLOAT,
'double': vtk.VTK_DOUBLE
}
# Import build options from ITK. Explicitly reference itk.Vector so
# that the itk::Vector type information is available when
# itk.BuildOptions is imported, otherwise we get an exception when
# importing itkBuildOptions. This is a workaround for a bug in ITK.
itk.Vector
import itkBuildOptions
# Select the best supported type available in the wrapping.
for (vtk_type, possible_image_types) in py2to3.iteritems(type_map):
type_map[vtk_type] = None
for possible_type in possible_image_types:
if itk.ctype(possible_type) in itkBuildOptions.SCALARS:
_vtk_cast_types[vtk_type] = ctype_to_vtk[possible_type]
break
return _vtk_cast_types
label_map = np.array(label_map)
assign_img = "label_map=label_map[0:1," + ",".join(
prediction_shape) + "]"
exec(assign_img)
#THE NUMBER OF CHANNELS OF THE OUTPUT ARE GIVEN BY THE NEURAL NET
Dimension = itk.template(img)[1][1]
label_map = np.array(label_map[0][0])
PixelDimension = label_map.shape[-1]
if (PixelDimension < 7):
if (PixelDimension >= 3 and os.path.splitext(
img_obj["out"])[1] not in ['.jpg', '.png']):
ComponentType = itk.ctype('float')
PixelType = itk.Vector[ComponentType,
PixelDimension]
elif (PixelDimension == 3):
PixelType = itk.RGBPixel.UC
label_map = np.absolute(label_map)
label_map = np.around(label_map).astype(np.uint16)
else:
PixelType = itk.ctype(out_ctype)
# label_map = np.absolute(label_map)
# label_map = np.around(label_map).astype(np.uint16)
OutputImageType = itk.Image[PixelType, Dimension]
out_img = OutputImageType.New()
else:
# 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 platform
Dimension = 3
CoordType = itk.ctype('float')
# Windows requires unsigned long long for 64-bit identifiers
if platform.system() == 'Windows':
ElementIdentifierType = itk.ctype('unsigned long long')
else:
ElementIdentifierType = itk.ctype('unsigned long')
PointSetType = itk.PointSet[CoordType, Dimension]
pointSet = PointSetType.New()
points = pointSet.GetPoints()
# Create points
p0 = itk.Point[CoordType, Dimension]()
p1 = itk.Point[CoordType, Dimension]()
def Convert_USRF_BMode(inputFilePath):
Input_Dir, Input_File = os.path.split(inputFilePath)
Input_FileName, Input_FileExt = os.path.splitext(Input_File)
## Set Output Dir
Out_Dir = Input_Dir +'/BMode/'
if not os.path.exists(Out_Dir):
os.makedirs(Out_Dir)
## Load RF data
InputPixelType = itk.ctype('float')
Dimension = 2
ImageType = itk.Image[InputPixelType, Dimension]
direction = 0
reader = itk.ImageFileReader[ImageType].New()
reader.SetFileName(inputFilePath)
# Remove low frequency artifacts on the Interson array probe
filterFunction = itk.ButterworthBandpass1DFilterFunction.New()
# < 2 MHz
filterFunction.SetLowerFrequency( 0.12 )
filterFunction.SetOrder( 7 )
ComplexPixelType = itk.complex[InputPixelType]
ComplexImageType = itk.Image[ComplexPixelType, Dimension]
frequencyFilter = itk.FrequencyDomain1DImageFilter[ComplexImageType,
ComplexImageType].New()
frequencyFilter.SetDirection(direction)
filterFunctionBase = itk.FrequencyDomain1DFilterFunction.New()
frequencyFilter.SetFilterFunction(filterFunctionBase.cast(filterFunction))
## Generate Pre-Scanconversion Data from the RF file
bMode_Filter = itk.BModeImageFilter[ImageType, ImageType].New()
bMode_Filter.SetInput(reader.GetOutput())
bMode_Filter.SetDirection(direction)
bMode_Filter.SetFrequencyFilter(frequencyFilter)
bMode_Filter.Update()
bMode = bMode_Filter.GetOutput()
writerInput = bMode
# Apply missing spacing information
if 'LinearProbe' in inputFilePath:
changeInformation = itk.ChangeInformationImageFilter.New(bMode)
changeInformation.SetUseReferenceImage(True)
changeInformation.SetChangeSpacing(True)
output_spacing = [1.0, 1.0]
size = itk.size(bMode)
sos = 1540.
fs = 30000.
output_spacing[0] = sos / (2 * fs)
transducer_width = 30.0
output_spacing[1] = transducer_width / (size[1] - 1)
changeInformation.SetOutputSpacing(output_spacing)
writerInput = changeInformation.GetOutput()
outputFilePath = Out_Dir + Input_FileName + '_BMode.mha'
print(outputFilePath)
writer = itk.ImageFileWriter[ImageType].New()
writer.SetFileName(outputFilePath)
writer.SetInput(writerInput)
writer.SetUseCompression(True)
writer.Update()
# 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
itk.auto_progress(2)
PixelType = itk.ctype("float")
Dimension = 2
ImageType = itk.Image[PixelType, Dimension]
reader = itk.ImageFileReader[ImageType].New()
reader.SetFileName(sys.argv[1])
diffusion = itk.GradientAnisotropicDiffusionImageFilter.New(Input=reader.GetOutput())
diffusion.SetTimeStep(0.0625)
diffusion.SetConductanceParameter(9.0)
diffusion.SetNumberOfIterations(5)
gradient = itk.GradientMagnitudeImageFilter.New(Input=diffusion.GetOutput())
watershed = itk.WatershedImageFilter.New(Input=gradient.GetOutput())
watershed.SetThreshold(0.01)
watershed.SetLevel(0.2)
# 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)
# 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 sys
import itk
import argparse
parser = argparse.ArgumentParser(
description="Overlay Label Map On Top Of An Image.")
parser.add_argument("input_image")
parser.add_argument("label_map")
parser.add_argument("output_image")
args = parser.parse_args()
PixelType = itk.ctype("unsigned char")
Dimension = 2
ImageType = itk.Image[PixelType, Dimension]
reader = itk.ImageFileReader[ImageType].New()
reader.SetFileName(args.input_image)
labelReader = itk.ImageFileReader[ImageType].New()
labelReader.SetFileName(args.label_map)
LabelType = itk.ctype("unsigned long")
LabelObjectType = itk.StatisticsLabelObject[LabelType, Dimension]
LabelMapType = itk.LabelMap[LabelObjectType]
converter = itk.LabelImageToLabelMapFilter[ImageType, LabelMapType].New()
# 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="Process A 2D Slice Of A 3D Image.")
parser.add_argument("input_3D_image")
parser.add_argument("output_3D_image")
parser.add_argument("slice_number", type=int)
args = parser.parse_args()
Dimension = 3
PixelType = itk.ctype("short")
ImageType = itk.Image[PixelType, Dimension]
reader = itk.ImageFileReader[ImageType].New()
reader.SetFileName(args.input_3D_image)
reader.Update()
inputImage = reader.GetOutput()
extractFilter = itk.ExtractImageFilter.New(inputImage)
extractFilter.SetDirectionCollapseToSubmatrix()
# set up the extraction region [one slice]
inputRegion = inputImage.GetBufferedRegion()
size = inputRegion.GetSize()
size[2] = 1 # we extract along z direction
start = inputRegion.GetIndex()
#%%
if __name__ == "__main__":
args = parse_args()
DicomDir = args.indir
outdir = args.outdir
# Parameters
sigmaSmallScale = 1.5
sigmasLargeScale = [0.6, 0.8]
lowerThreshold = 25
upperThreshold = 600
# Useful shortcut
ShortType = itk.ctype('short')
UShortType = itk.ctype('unsigned short')
UCType = itk.ctype('unsigned char')
FType = itk.ctype('float')
ULType = itk.ctype('unsigned long')
FloatImageType = itk.Image[FType,3]
ShortImageType = itk.Image[ShortType,3]
UCImageType = itk.Image[UCType,3]
ULImageType = itk.Image[ULType,3]
# Read Dicoms Series and turn it into 3D object
inputCT, Inputmetadata = dicomsTo3D(DicomDir, ShortType)
# showSome(inputCT,50)
print("Preprocessing")
#%%
#!/usr/bin/env python
# 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.
import itk
Dimension = 2
RGBPixelType = itk.RGBPixel[itk.ctype('unsigned char')]
image = itk.Image[RGBPixelType, Dimension].New()
import numpy as np
import time
import itk
start = time.time()
# PixelType = itk.ctype('unsigned short')
# ImageType = itk.Image[PixelType, Dimension]
Dimension = 3
RealType = itk.ctype('float')
RealImageType = itk.Image[RealType, Dimension]
LabelImageType = itk.Image[itk.ctype('unsigned short'), Dimension]
readerType = itk.ImageFileReader[RealImageType]
reader = readerType.New()
reader.SetFileName(
'C:/Users/duso/PycharmProjects/Semestralni_Prace/Registration/Frangis/RegistrationTests/test/Reg_8_GaborliversSmall.nrrd'
)
reader.Update()
#
image = reader.GetOutput()
#
origin = np.zeros(3)
spacing = np.ones(3)
size = np.ones(3, np.int) * 64
mean = np.zeros(3)
sigma = np.zeros(3)
def TestComplex(selectedType):
cType = itk.complex[itk.ctype(selectedType)]
c = cType(2.0, 3.0)
assert c.real() == 2.0
assert c.imag() == 3.0
assert complex(c) == 2.0 + 3.0j
import sys
import os
import itk
if len(sys.argv) < 2:
print("Usage: " + sys.argv[0] +
" [DicomDirectory [outputFileName [seriesName]]]")
print("If DicomDirectory is not specified, current directory is used\n")
# current directory by default
dirName = '.'
if len(sys.argv) > 1:
dirName = sys.argv[1]
PixelType = itk.ctype('signed short')
Dimension = 3
ImageType = itk.Image[PixelType, Dimension]
namesGenerator = itk.GDCMSeriesFileNames.New()
namesGenerator.SetUseSeriesDetails(True)
namesGenerator.AddSeriesRestriction("0008|0021")
namesGenerator.SetGlobalWarningDisplay(False)
namesGenerator.SetDirectory(dirName)
seriesUID = namesGenerator.GetSeriesUIDs()
if len(seriesUID) < 1:
print('No DICOMs in: ' + dirName)
sys.exit(1)
def rigid_body_transform3D(input_filename,output_filename,rot=[0.,0.,0.],t=[0.,0.,0.],cor=[0.,0.,0.],threshold=0.,default_pixel_value=0,min_out=0,max_out=255,verbose=False):
"""This function makes a rigid body transform on the 3D volume of image and writes it in the output"""
# input_filename = '/Volumes/Storage/Projects/Registration/QuickData/OA-BEADS-CT.nii'
# output_filename = '/Volumes/Storage/Projects/Registration/QuickData/transformed_ct.nii'
#
# threshold = 0.
# default_pixel_value = 100
# min_out = 0
# max_out = 255
#
# rot = [40. ,0. ,0.] # Rotation in degrees in x, y, and z direction.
# t = [100. ,100. ,100.] # translation in x, y, and z directions.
# cor = [0. ,0. ,0.] # offset of the rotation from the center of image (3D)
#
#
# verbose = False # Verbose details of all steps.
#
#% ------------------------------- Import Libraries ------------------------
import itk # imports insight Toolkit
import numpy
import sys
#import StereoFlouroscopyRegistration.util.itk_helpers as Functions
#%%------------------ Starting the main body of the code ----------------
# -------------------- Reader -------------------------
InputPixelType = itk.ctype("short")
DimensionIn = 3
InputImageType = itk.Image[InputPixelType , DimensionIn ]
ReaderType = itk.ImageFileReader[InputImageType]
reader = ReaderType.New()
reader.SetFileName(input_filename)
try:
print("Reading image: " + input_filename)
reader.Update()
print("Image Read Successfully")
except ValueError:
print("ERROR: ExceptionObject cauth! \n")
print(ValueError)
sys.exit()
inputImage = reader.GetOutput()
if verbose :
print(inputImage)
inOrigin = inputImage.GetOrigin() # Get the origin of the image.
inSpacing = inputImage.GetSpacing() # Get the resolution of the input image.
inSize = inputImage.GetBufferedRegion().GetSize() # Get the size of the input image.
inDirection = inputImage.GetDirection()
#%% ------------------ Transformation
# This part is inevitable since the interpolator (Ray-cast) and resample Image
# image filter uses a Transformation -- Here we set it to identity.
TransformType = itk.CenteredEuler3DTransform[itk.D]
transform = TransformType.New()
direction_mat = get_vnl_matrix(inDirection.GetVnlMatrix())
rot = numpy.dot(-1,rot) # Due to Direction of transform mapping ( 8.3.1 in the ITK manual)
t = numpy.dot(-1,t ) # Due to Direction of transform mapping ( 8.3.1 in the ITK manual)
rot = direction_mat.dot(numpy.transpose(rot))
t = direction_mat.dot(numpy.transpose(t ))
transform.SetRotation(numpy.deg2rad(rot[0]),numpy.deg2rad(rot[1]),numpy.deg2rad(rot[2])) # Setting the rotation of the transform
transform.SetTranslation(itk.Vector.D3(t)) # Setting the translation of the transform
transform.SetComputeZYX(True) # The order of rotation will be ZYX.
center = direction_mat.dot(inOrigin)+ numpy.multiply(inSpacing,inSize)/2. # Setting the center of rotation as center of 3D object + offset determined by cor.
center = direction_mat.dot(center)-t # Convert the image to the local coordinate system.
transform.SetCenter(center) # Setting the center of rotation.
if verbose :
print(transform)
#% ------------------ Interpolator ------------------------------------
InterpolatorType = itk.LinearInterpolateImageFunction[InputImageType,itk.D]
interpolator = InterpolatorType.New()
#%%----------------- Resample Image Filter -----------------------
# In this part the resample image filter to map a 3D image to 2D image plane with desired specs is designed
FilterType = itk.ResampleImageFilter[InputImageType,InputImageType] # Defining the resample image filter type.
resamplefilter = FilterType.New() # Pointer to the filter
# R = Functions.get_transform_direction(transform)
# T = numpy.transpose(t)
#
outOrigin = inOrigin - t #+ R.dot(numpy.transpose(direction_mat.dot(inOrigin)))
# transform_matrix = Functions.get_vnl_matrix(transform.GetMatrix().GetVnlMatrix())
#outDirection = transform_matrix.dot(direction_mat)
outDirection = inDirection
scaling = 1 # the scaling factor for the image.
outSize= [scaling*inSize[0],scaling*inSize[1],inSize[2]]
resamplefilter.SetInput(inputImage) # Setting the input image data
resamplefilter.SetDefaultPixelValue(default_pixel_value) # Setting the default Pixel value
resamplefilter.SetInterpolator(interpolator) # Setting the interpolator
resamplefilter.SetTransform(transform) # Setting the transform
resamplefilter.SetSize(outSize) # Setting the size of the output image.
resamplefilter.SetOutputSpacing(inSpacing) # Setting the spacing(resolution) of the output image.
# Functions.change_image_direction(resamplefilter.GetOutputDirection(),outDirection,3)
resamplefilter.SetOutputOrigin(outOrigin) # Setting the output origin of the image
resamplefilter.SetOutputDirection(outDirection) # Setting the output direction of the image.
resamplefilter.Update() # Updating the resample image filter.
if verbose:
print(resamplefilter)
#%% ------------------ Writer ------------------------------------
# The output of the resample filter can then be passed to a writer to
# save the DRR image to a file.
WriterType=itk.ImageFileWriter[InputImageType]
writer=WriterType.New()
writer.SetFileName(output_filename)
writer.SetInput(resamplefilter.GetOutput())
try:
print("Writing the transformed Volume at : " + output_filename)
writer.Update()
print("Volume Printed Successfully")
except ValueError:
print("ERROR: ExceptionObject caugth! \n")
print(ValueError)
sys.exit()
import sys
import os
import itk
if len(sys.argv) < 2:
print("Usage: " + sys.argv[0] +
" [DicomDirectory [outputFileName [seriesName]]]")
print("If DicomDirectory is not specified, current directory is used\n")
# current directory by default
dirName = "."
if len(sys.argv) > 1:
dirName = sys.argv[1]
PixelType = itk.ctype("signed short")
Dimension = 3
ImageType = itk.Image[PixelType, Dimension]
namesGenerator = itk.GDCMSeriesFileNames.New()
namesGenerator.SetUseSeriesDetails(True)
namesGenerator.AddSeriesRestriction("0008|0021")
namesGenerator.SetGlobalWarningDisplay(False)
namesGenerator.SetDirectory(dirName)
seriesUID = namesGenerator.GetSeriesUIDs()
if len(seriesUID) < 1:
print("No DICOMs in: " + dirName)
sys.exit(1)
# since higher values quickly start to undersegment the image.
import sys
import itk
if len(sys.argv) != 5:
print('Usage: ' + sys.argv[0] +
' <InputFileName> <OutputFileName> <Threshold> <Level>')
sys.exit(1)
inputFileName = sys.argv[1]
outputFileName = sys.argv[2]
Dimension = 2
FloatPixelType = itk.ctype('float')
FloatImageType = itk.Image[FloatPixelType, Dimension]
reader = itk.ImageFileReader[FloatImageType].New()
reader.SetFileName(inputFileName)
gradientMagnitude = \
itk.GradientMagnitudeImageFilter.New(Input=reader.GetOutput())
watershed = \
itk.WatershedImageFilter.New(Input=gradientMagnitude.GetOutput())
threshold = float(sys.argv[3])
level = float(sys.argv[4])
watershed.SetThreshold(threshold)
watershed.SetLevel(level)
def TestComplex(selectedType):
cType = itk.complex[itk.ctype(selectedType)]
c = cType(2.0,3.0)
assert c.real() == 2.0
assert c.imag() == 3.0
assert complex(c) == 2.0+3.0j
# 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
Dimension = 2
PixelType = itk.ctype("unsigned char")
ImageType = itk.Image[PixelType, Dimension]
def create_fixed_image():
start = [
0,
] * Dimension
size = [
100,
] * Dimension
region = itk.ImageRegion[Dimension](start, size)
ImageType = itk.Image[PixelType, Dimension]
image = ImageType.New()
import sys
import itk
if len(sys.argv) != 6:
print('Usage: ' + sys.argv[0] + ' <InputFileName> <OutputFileName> <Alpha> <Beta> <Radius>')
sys.exit(1)
inputFileName = sys.argv[1]
outputFileName = sys.argv[2]
alpha = float(sys.argv[3])
beta = float(sys.argv[4])
radiusValue = int(sys.argv[5])
Dimension = 2
PixelType = itk.ctype('unsigned char')
ImageType = itk.Image[PixelType, Dimension]
reader = itk.ImageFileReader[ImageType].New()
reader.SetFileName(inputFileName)
histogramEqualization = \
itk.AdaptiveHistogramEqualizationImageFilter.New(reader)
histogramEqualization.SetAlpha(alpha)
histogramEqualization.SetBeta(beta)
radius = itk.Size[Dimension]()
radius.Fill(radiusValue);
histogramEqualization.SetRadius(radius)
itk.imwrite(histogramEqualization, outputFileName)
# test class_
assert itk.class_(reader) == ReaderType
assert itk.class_("dummy") == str
# test template
assert itk.template(ReaderType) == (itk.ImageFileReader, (ImageType,))
assert itk.template(reader) == (itk.ImageFileReader, (ImageType,))
try:
itk.template(str)
raise Exception("unknown class should send an exception")
except KeyError:
pass
# test ctype
assert itk.ctype("unsigned short") == itk.US
assert itk.ctype(" unsigned \n short \t ") == itk.US
assert itk.ctype("signed short") == itk.SS
assert itk.ctype("short") == itk.SS
try:
itk.ctype("dummy")
raise Exception("unknown C type should send an exception")
except KeyError:
pass
# test output
assert itk.output(reader) == reader.GetOutput()
assert itk.output(1) == 1
# test the deprecated image
assert itk.image(reader) == reader.GetOutput()
def Analyze_USRF_Spectra(spectra_filepath, output_path, spectra_start_idx=4,
spectra_stop_idx=21):
ComponentType = itk.ctype('float')
Dimension = 2
ImageType = itk.VectorImage[ComponentType, Dimension]
reader = itk.ImageFileReader[ImageType].New()
reader.SetFileName(spectra_filepath)
reader.UpdateLargestPossibleRegion()
spectra_image = reader.GetOutput()
# lateral x axial x spectral component
spectra_array = itk.GetArrayFromImage(spectra_image)
# Look at low noise bandwidth of interest
spectra_bandwidth = spectra_array[..., spectra_start_idx:spectra_stop_idx]
# Work with spectra in dB
spectra = 10 * np.log10(spectra_bandwidth)
output_path = os.path.join(os.path.dirname(spectra_filepath), output_path)
if not os.path.exists(output_path):
os.makedirs(output_path)
basename = os.path.basename(spectra_filepath).split('_Spectra')[0]
def save_output(arr, identifier):
output_filepath = os.path.join(output_path, basename + identifier)
np.save(output_filepath + '.npy', arr)
save_output(spectra, '_Spectra')
# Prep images
num_lateral = spectra.shape[0]
num_axial = spectra.shape[1]
chebyshev_num_features = 7
chebyshev_image = np.zeros([num_lateral, num_axial, chebyshev_num_features], dtype=np.float32)
line_num_features = 2
line_image = np.zeros([num_lateral, num_axial, line_num_features], dtype=np.float32)
local_slope_points = 4
local_slope_num_features = spectra.shape[2] / local_slope_points
local_slope_image = np.zeros([num_lateral, num_axial, local_slope_num_features],
dtype=np.float32)
legendre_num_features = 7
legendre_image = np.zeros([num_lateral, num_axial, legendre_num_features], dtype=np.float32)
integrated_num_features = 1
integrated_image = np.zeros([num_lateral, num_axial, integrated_num_features], dtype=np.float32)
for lateral in np.arange(num_lateral):
print "Line ", lateral, " of ", num_lateral
for axial in np.arange(num_axial):
spectrum = spectra[lateral, axial, :].ravel()
cheb_coefs = np.polynomial.chebyshev.Chebyshev.fit(
np.arange(len(spectrum)), spectrum, chebyshev_num_features-1)
chebyshev_image[lateral, axial, :] = cheb_coefs.coef
line_coefs = scipy.stats.linregress(
np.arange(len(spectrum)), spectrum)
line_image[lateral, axial, 0] = line_coefs.slope
line_image[lateral, axial, 1] = line_coefs.intercept
for ii in range(local_slope_num_features):
coefs = scipy.stats.linregress(
np.arange(local_slope_points),
spectrum[ii*local_slope_points:(ii+1)*local_slope_points])
local_slope_image[lateral, axial, ii] = coefs.slope
legn_coefs = np.polynomial.legendre.legfit(
np.arange(len(spectrum)), spectrum, legendre_num_features-1)
legendre_image[lateral, axial, :] = legn_coefs
intg_coefs = np.sum(spectrum)
integrated_image[lateral, axial, 0] = intg_coefs
save_output(chebyshev_image, '_Chebyshev')
save_output(line_image, '_Line')
save_output(local_slope_image, '_LocalSlope')
save_output(legendre_image, '_Legendre')
save_output(integrated_image, '_Integrated')
# test class_
assert itk.class_(reader) == ReaderType
assert itk.class_("dummy") == str
# test template
assert itk.template(ReaderType) == (itk.ImageFileReader, (ImageType, ))
assert itk.template(reader) == (itk.ImageFileReader, (ImageType, ))
try:
itk.template(str)
raise Exception("unknown class should send an exception")
except KeyError:
pass
# test ctype
assert itk.ctype("unsigned short") == itk.US
assert itk.ctype(" unsigned \n short \t ") == itk.US
assert itk.ctype("signed short") == itk.SS
assert itk.ctype("short") == itk.SS
try:
itk.ctype("dummy")
raise Exception("unknown C type should send an exception")
except KeyError:
pass
# test output
assert itk.output(reader) == reader.GetOutput()
assert itk.output(1) == 1
# test the deprecated image
assert itk.image(reader) == reader.GetOutput()
assert itk.image(1) == 1
#!/usr/bin/env python
# ==========================================================================
#
# 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.
#
# ==========================================================================*/
import itk
Dimension = 2
RGBPixelType = itk.RGBPixel[itk.ctype("unsigned char")]
image = itk.Image[RGBPixelType, Dimension].New()
try:
input_matrix2 = itk.GetArrayFromImage(input_volume)
except:
print(0)
try:
input_matrix2 = sitk.GetArrayFromImage(input_volume)
except:
print(0)
input_matrix_original = input_matrix2
input_volume2 = input_matrix2
blobs2 = np.zeros(input_volume2.shape, dtype=bool)
#do binary threshoulding on the original image
PixelType = itk.ctype('signed short')
Dimension = 3
try:
thresholdFilter = sitk.BinaryThresholdImageFilter()
input_volume_thr = thresholdFilter.Execute(input_volume, low, high, 255, 0)
except:
print(0)
try:
ImageType_threshold = itk.Image[PixelType, Dimension]
thresholdFilter = itk.BinaryThresholdImageFilter[
ImageType_threshold, ImageType_threshold].New()
# input_volume=thresholdFilter.Execute(input_volume,low,high,0,255)
thresholdFilter.SetInput(input_volume)
thresholdFilter.SetLowerThreshold(low)
thresholdFilter.SetUpperThreshold(high)
def Convert_USRF_Spectra(input_filepath, reference_path, output_path, side_lines=5, fft1D_size=128):
InputPixelType = itk.ctype('float')
Dimension = 2
ImageType = itk.Image[InputPixelType, Dimension]
reader = itk.ImageFileReader[ImageType].New()
reader.SetFileName(input_filepath)
input_RF = reader.GetOutput()
# Apply missing spacing information
if 'LinearProbe' in input_filepath:
change_information = itk.ChangeInformationImageFilter.New(input_RF)
change_information.SetUseReferenceImage(True)
change_information.SetChangeSpacing(True)
output_spacing = [1.0, 1.0]
size = itk.size(input_RF)
sos = 1540.
fs = 30000.
output_spacing[0] = sos / (2 * fs)
transducer_width = 30.0
output_spacing[1] = transducer_width / (size[1] - 1)
change_information.SetOutputSpacing(output_spacing)
input_RF = change_information.GetOutput()
input_RF.UpdateOutputInformation()
SideLinesPixelType = itk.ctype('unsigned char')
SideLinesImageType = itk.Image[SideLinesPixelType, Dimension]
side_lines_image = SideLinesImageType.New()
side_lines_image.CopyInformation(input_RF)
side_lines_image.SetRegions(input_RF.GetLargestPossibleRegion())
side_lines_image.Allocate()
side_lines_image.FillBuffer(side_lines)
spectra_window_filter = itk.Spectra1DSupportWindowImageFilter.New(side_lines_image)
spectra_window_filter.SetFFT1DSize(fft1D_size)
spectra_window_filter.SetStep(8)
spectra_window_filter.UpdateLargestPossibleRegion()
support_window_image = spectra_window_filter.GetOutput()
SpectraImageType = itk.VectorImage[InputPixelType, Dimension]
reference_reader = itk.ImageFileReader[SpectraImageType].New()
basename = os.path.splitext(os.path.basename(input_filepath))[0]
reference_path = os.path.join(os.path.dirname(input_filepath), reference_path)
reference_glob = glob.glob(os.path.join(reference_path, basename[:-11] + '*.mha'))
if len(reference_glob) != 1:
print('Reference spectra file not found')
print('Found: ', reference_glob)
sys.exit(1)
reference_reader.SetFileName(reference_glob[0])
reference_reader.UpdateLargestPossibleRegion()
reference_spectra_image = SpectraImageType.New()
reference_spectra_image.CopyInformation(support_window_image)
reference_spectra_image.SetRegions(support_window_image.GetLargestPossibleRegion())
reference_spectra_image.SetNumberOfComponentsPerPixel(reference_reader.GetOutput().GetNumberOfComponentsPerPixel())
reference_spectra_image.Allocate()
reference_index = itk.Index[Dimension]()
reference_index.Fill(0)
reference_spectrum = reference_reader.GetOutput().GetPixel(reference_index)
reference_spectra_image.FillBuffer(reference_spectrum)
spectra_filter = itk.Spectra1DImageFilter.New(input_RF)
spectra_filter.SetSupportWindowImage(spectra_window_filter.GetOutput())
spectra_filter.SetReferenceSpectraImage(reference_spectra_image)
spectra_filter.UpdateLargestPossibleRegion()
output_path = os.path.join(os.path.dirname(input_filepath), output_path)
if not os.path.exists(output_path):
os.makedirs(output_path)
identifier = '_Spectra_side_lines_{:02d}_fft1d_size_{:03d}.mha'.format(side_lines, fft1D_size)
output_filepath = os.path.join(output_path, basename + identifier)
writer = itk.ImageFileWriter.New(spectra_filter)
writer.SetFileName(output_filepath)
writer.Update()
writer.SetFileName( newpath+'/fixed.png' )
writer.SetInput( filter.GetOutput() )
writer.Update()
filter = itk.RescaleIntensityImageFilter[MovingImageType, OutPutImageType].New()
filter.SetOutputMinimum( 0 )
filter.SetOutputMaximum(255)
filter.SetInput(movingImage )
writer.SetFileName( newpath+'/moving.png' )
writer.SetInput( filter.GetOutput() )
writer.Update()
OutputPixelType = itk.ctype('unsigned char')
OutputImageType = itk.Image[OutputPixelType, 2]
caster = itk.CastImageFilter[FixedImageType,
OutputImageType].New(Input=resampler)
newpath = 'C:/Users/duso/OneDrive - AIMTEC a. s/Documents/ZDO/SEM/X_01'
if not os.path.exists(newpath):
os.makedirs(newpath)
outputImageFile = 'C:/Users/duso/OneDrive - AIMTEC a. s/Documents/ZDO/SEM/X_01/res.png'
differenceImageAfterFile = 'C:/Users/duso/OneDrive - AIMTEC a. s/Documents/ZDO/SEM/X_01/difAfter.png'
differenceImageBeforeFile = 'C:/Users/duso/OneDrive - AIMTEC a. s/Documents/ZDO/SEM/X_01/difBefore.png'
writer = itk.ImageFileWriter.New(Input=caster, FileName=outputImageFile)
writer.SetFileName(outputImageFile)
writer.Update()
import sys
import os
import itk
if len(sys.argv) < 2:
print("Usage: " + sys.argv[0] + " [DicomDirectory [outputFileName [seriesName]]]")
print("If DicomDirectory is not specified, current directory is used\n")
# current directory by default
dirName = "."
if len(sys.argv) > 1:
dirName = sys.argv[1]
PixelType = itk.ctype("signed short")
Dimension = 3
ImageType = itk.Image[PixelType, Dimension]
namesGenerator = itk.GDCMSeriesFileNames.New()
namesGenerator.SetUseSeriesDetails(True)
namesGenerator.AddSeriesRestriction("0008|0021")
namesGenerator.SetGlobalWarningDisplay(False)
namesGenerator.SetDirectory(dirName)
seriesUID = namesGenerator.GetSeriesUIDs()
if len(seriesUID) < 1:
print("No DICOMs in: " + dirName)
sys.exit(1)