def verify_same_geometry(img_1: sitk.Image, img_2: sitk.Image):
ori1, spacing1, direction1, size1 = img_1.GetOrigin(), img_1.GetSpacing(
), img_1.GetDirection(), img_1.GetSize()
ori2, spacing2, direction2, size2 = img_2.GetOrigin(), img_2.GetSpacing(
), img_2.GetDirection(), img_2.GetSize()
same_ori = np.all(np.isclose(ori1, ori2))
if not same_ori:
print("the origin does not match between the images:")
print(ori1)
print(ori2)
same_spac = np.all(np.isclose(spacing1, spacing2))
if not same_spac:
print("the spacing does not match between the images")
print(spacing1)
print(spacing2)
same_dir = np.all(np.isclose(direction1, direction2))
if not same_dir:
print("the direction does not match between the images")
print(direction1)
print(direction2)
same_size = np.all(np.isclose(size1, size2))
if not same_size:
print("the size does not match between the images")
print(size1)
print(size2)
if same_ori and same_spac and same_dir and same_size:
return True
else:
return False
def assert_sitk_img_equivalence(img: SimpleITK.Image,
img_ref: SimpleITK.Image):
assert img.GetDimension() == img_ref.GetDimension()
assert img.GetSize() == img_ref.GetSize()
assert img.GetOrigin() == img_ref.GetOrigin()
assert img.GetSpacing() == img_ref.GetSpacing()
assert (img.GetNumberOfComponentsPerPixel() ==
img_ref.GetNumberOfComponentsPerPixel())
assert img.GetPixelIDValue() == img_ref.GetPixelIDValue()
assert img.GetPixelIDTypeAsString() == img_ref.GetPixelIDTypeAsString()
def compatible_metadata(image1: sitk.Image,
image2: sitk.Image,
check_size: bool = True,
check_spacing: bool = True,
check_origin: bool = True) -> bool:
""" Compares the metadata of two images and determines if all checks are successful or not.
Comparisons are carried out with a small tolerance (0.0001).
@param image1: first image
@param image2: second image
@param check_size: if true, check if the sizes of the images are equal
@param check_spacing: if true, check if the spacing of the images are equal
@param check_origin: if true, check if the origin of the images are equal
@return: true, if images are equal in all given checks, false if one of them failed
"""
all_parameters_equal = True
tolerance = 1e-4
if check_size:
size1 = image1.GetSize()
size2 = image2.GetSize()
if size1 != size2:
all_parameters_equal = False
print(f'Images do not have the same size ({size1} != {size2})')
if check_spacing:
spacing1 = image1.GetSpacing()
spacing2 = image2.GetSpacing()
if any(
list(
abs(s1 - s2) > tolerance
for s1, s2 in zip(spacing1, spacing2))):
all_parameters_equal = False
print(
f'Images do not have the same spacing ({spacing1} != {spacing2})'
)
if check_origin:
origin1 = image1.GetOrigin()
origin2 = image2.GetOrigin()
if any(
list(
abs(o1 - o2) > tolerance
for o1, o2 in zip(origin1, origin2))):
all_parameters_equal = False
print(
f'Images do not have the same origin ({origin1} != {origin2})')
return all_parameters_equal
def split_4d_itk(img_itk: sitk.Image) -> List[sitk.Image]:
"""
Helper function to split 4d itk images into multiple 3 images
Args:
img_itk: 4D input image
Returns:
List[sitk.Image]: 3d output images
"""
img_npy = sitk.GetArrayFromImage(img_itk)
spacing = img_itk.GetSpacing()
origin = img_itk.GetOrigin()
direction = np.array(img_itk.GetDirection()).reshape(4, 4)
spacing = tuple(list(spacing[:-1]))
assert len(spacing) == 3
origin = tuple(list(origin[:-1]))
assert len(origin) == 3
direction = tuple(direction[:-1, :-1].reshape(-1))
assert len(direction) == 9
images_new = []
for i, t in enumerate(range(img_npy.shape[0])):
img = img_npy[t]
images_new.append(
create_itk_image_spatial_props(img, spacing, origin, direction))
return images_new
def image_resample(image: sitk.Image):
'''
image = sitk.ReadImage(image_path)
使用 SimpleITK 自带函数重新缩放图像
'''
origin_spacing = image.GetSpacing() # 获取源分辨率
origin_size = image.GetSize()
new_spacing = [1, 1, 1] # 设置新分辨率
resample = sitk.ResampleImageFilter()
resample.SetInterpolator(sitk.sitkLinear)
resample.SetDefaultPixelValue(0)
resample.SetOutputSpacing(new_spacing)
resample.SetOutputOrigin(image.GetOrigin())
resample.SetOutputDirection(image.GetDirection())
# 计算新图像的大小
new_size = [
int(np.round(origin_size[0] * (origin_spacing[0] / new_spacing[0]))),
int(np.round(origin_size[1] * (origin_spacing[1] / new_spacing[1]))),
int(np.round(origin_size[2] * (origin_spacing[2] / new_spacing[2])))
]
resample.SetSize(new_size)
new_image = resample.Execute(image)
return new_image
def sitk_to_nib(
image: sitk.Image,
keepdim: bool = False,
) -> Tuple[np.ndarray, np.ndarray]:
data = sitk.GetArrayFromImage(image).transpose()
num_components = image.GetNumberOfComponentsPerPixel()
if num_components == 1:
data = data[np.newaxis] # add channels dimension
input_spatial_dims = image.GetDimension()
if input_spatial_dims == 2:
data = data[..., np.newaxis]
if not keepdim:
data = ensure_4d(data, num_spatial_dims=input_spatial_dims)
assert data.shape[0] == num_components
assert data.shape[1:1 + input_spatial_dims] == image.GetSize()
spacing = np.array(image.GetSpacing())
direction = np.array(image.GetDirection())
origin = image.GetOrigin()
if len(direction) == 9:
rotation = direction.reshape(3, 3)
elif len(direction) == 4: # ignore first dimension if 2D (1, W, H, 1)
rotation_2d = direction.reshape(2, 2)
rotation = np.eye(3)
rotation[:2, :2] = rotation_2d
spacing = *spacing, 1
origin = *origin, 0
else:
raise RuntimeError(f'Direction not understood: {direction}')
rotation = np.dot(FLIP_XY, rotation)
rotation_zoom = rotation * spacing
translation = np.dot(FLIP_XY, origin)
affine = np.eye(4)
affine[:3, :3] = rotation_zoom
affine[:3, 3] = translation
return data, affine
def centre_of_mass(image: sitk.Image) -> np.ndarray:
r""" Compute the centre of mass of the image.
A real-valued image represents the distribution of mass, and its
centre of mass is defined as :math:`\frac{1}{\sum_p I(p)} \sum_p p I(p)`.
Parameters
----------
image : sitk.Image
Input image.
Returns
-------
np.ndarray
World coordinates (x, y, z) of the centre of mass.
"""
data = sitk.GetArrayViewFromImage(image)
grid = np.meshgrid(*[range(i) for i in data.shape], indexing='ij')
grid = np.vstack([a.flatten() for a in grid]).T
cm = np.average(grid, axis=0, weights=data.flatten())
return np.multiply(np.flip(cm, axis=0),
image.GetSpacing()) - image.GetOrigin()
def sitk_to_nib(
image: sitk.Image,
keepdim: bool = False,
) -> Tuple[np.ndarray, np.ndarray]:
data = sitk.GetArrayFromImage(image).transpose()
num_components = image.GetNumberOfComponentsPerPixel()
if num_components == 1:
data = data[np.newaxis] # add channels dimension
input_spatial_dims = image.GetDimension()
if not keepdim:
data = ensure_4d(data, False, num_spatial_dims=input_spatial_dims)
assert data.shape[0] == num_components
assert data.shape[-input_spatial_dims:] == image.GetSize()
spacing = np.array(image.GetSpacing())
direction = np.array(image.GetDirection())
origin = image.GetOrigin()
if len(direction) == 9:
rotation = direction.reshape(3, 3)
elif len(direction) == 4: # ignore first dimension if 2D (1, 1, H, W)
rotation_2d = direction.reshape(2, 2)
rotation = np.eye(3)
rotation[1:3, 1:3] = rotation_2d
spacing = 1, *spacing
origin = 0, *origin
rotation = np.dot(FLIP_XY, rotation)
rotation_zoom = rotation * spacing
translation = np.dot(FLIP_XY, origin)
affine = np.eye(4)
affine[:3, :3] = rotation_zoom
affine[:3, 3] = translation
return data, affine
def __init__(self, data: sitk.Image, segmentation: sitk.Image, bb_size):
self.segmentation = segmentation
# place bb so that the segmentation is centered
self.offset = (
(np.array(bb_size) - np.array(self.segmentation.GetSize())) /
2).astype(int)
# adjust offset if resulting bb is out of bounds
segmentation_origin_in_data = data.TransformPhysicalPointToIndex(
segmentation.GetOrigin())
self.offset = [
seg_or if seg_or - off < 0 else off
for seg_or, off in zip(segmentation_origin_in_data, self.offset)
]
self.offset = [
off + ((seg_or - off + bb_sz) - data_sz)
if seg_or - off + bb_sz > data_sz else off for seg_or, off, bb_sz,
data_sz in zip(segmentation_origin_in_data, self.offset, bb_size,
data.GetSize())
]
cropped_origin = np.array(segmentation_origin_in_data) - self.offset
assert all(cr_or >= 0 for cr_or in cropped_origin), \
f"Data size: {data.GetSize()}, BB size: {bb_size}, BB origin: {cropped_origin}"
assert all(cr_or + bb_s <= data_s for cr_or, bb_s, data_s in zip(cropped_origin, bb_size, data.GetSize())), \
f"Data size: {data.GetSize()}, BB size: {bb_size}, BB origin: {cropped_origin}"
self.data = data[cropped_origin[0]:cropped_origin[0] + bb_size[0],
cropped_origin[1]:cropped_origin[1] + bb_size[1],
cropped_origin[2]:cropped_origin[2] + bb_size[2]]
def slice_by_slice(image: sitk.Image, *args, **kwargs):
dim = image.GetDimension()
iter_dim = 2
if dim <= iter_dim:
image = func(image, *args, **kwargs)
return image
extract_size = list(image.GetSize())
extract_size[iter_dim:] = itertools.repeat(0, dim - iter_dim)
extract_index = [0] * dim
paste_idx = [slice(None, None)] * dim
extractor = sitk.ExtractImageFilter()
extractor.SetSize(extract_size)
if inplace:
for high_idx in itertools.product(
*[range(s) for s in image.GetSize()[iter_dim:]]):
extract_index[iter_dim:] = high_idx
extractor.SetIndex(extract_index)
paste_idx[iter_dim:] = high_idx
image[paste_idx] = func(extractor.Execute(image), *args,
**kwargs)
else:
img_list = []
for high_idx in itertools.product(
*[range(s) for s in image.GetSize()[iter_dim:]]):
extract_index[iter_dim:] = high_idx
extractor.SetIndex(extract_index)
paste_idx[iter_dim:] = high_idx
img_list.append(
func(extractor.Execute(image), *args, **kwargs))
for d in range(iter_dim, dim):
step = reduce((lambda x, y: x * y),
image.GetSize()[d + 1:], 1)
join_series_filter = sitk.JoinSeriesImageFilter()
join_series_filter.SetSpacing(image.GetSpacing()[d])
join_series_filter.SetOrigin(image.GetOrigin()[d])
img_list = [
join_series_filter.Execute(img_list[i::step])
for i in range(step)
]
assert len(img_list) == 1
image = img_list[0]
return image
def resample_sitk_image(sitk_image: sitk.Image,
new_size,
interpolator="gaussian",
fill_value=0) -> sitk.Image:
"""
modified version from:
https://github.com/jonasteuwen/SimpleITK-examples/blob/master/examples/resample_isotropically.py
"""
# if pass a path to image
if isinstance(sitk_image, str):
sitk_image = sitk.ReadImage(sitk_image)
assert (interpolator in _SITK_INTERPOLATOR_DICT.keys()
), "`interpolator` should be one of {}".format(
_SITK_INTERPOLATOR_DICT.keys())
if not interpolator:
interpolator = "linear"
pixelid = sitk_image.GetPixelIDValue()
if pixelid not in [1, 2, 4]:
raise NotImplementedError(
"Set `interpolator` manually, "
"can only infer for 8-bit unsigned or 16, 32-bit signed integers"
)
# 8-bit unsigned int
if sitk_image.GetPixelIDValue() == 1:
# if binary mask interpolate it as nearest
interpolator = "nearest"
sitk_interpolator = _SITK_INTERPOLATOR_DICT[interpolator]
orig_pixelid = sitk_image.GetPixelIDValue()
orig_origin = sitk_image.GetOrigin()
orig_direction = sitk_image.GetDirection()
# new spacing based on the desired output shape
new_spacing = tuple(
np.array(sitk_image.GetSpacing()) * np.array(sitk_image.GetSize()) /
np.array(new_size))
# setup image resampler - SimpleITK 2.0
resample_filter = sitk.ResampleImageFilter()
resample_filter.SetOutputSpacing(new_spacing)
resample_filter.SetSize(new_size)
resample_filter.SetOutputDirection(orig_direction)
resample_filter.SetOutputOrigin(orig_origin)
resample_filter.SetTransform(sitk.Transform())
resample_filter.SetDefaultPixelValue(orig_pixelid)
resample_filter.SetInterpolator(sitk_interpolator)
resample_filter.SetDefaultPixelValue(fill_value)
# run it
resampled_sitk_image = resample_filter.Execute(sitk_image)
return resampled_sitk_image
def match_world_info(source: sitk.Image,
target: sitk.Image,
spacing: Union[bool, Tuple[int], List[int]] = True,
origin: Union[bool, Tuple[int], List[int]] = True,
direction: Union[bool, Tuple[int], List[int]] = True):
"""Copy world information (eg spacing, origin, direction) from one
image object to another.
This matching is sometimes necessary for slight differences in
metadata perhaps from founding that may prevent ITK filters from executing.
Args:
source (:obj:`sitk.Image`): Source object whose relevant metadata
will be copied into ``target``.
target (:obj:`sitk.Image`): Target object whose corresponding
metadata will be overwritten by that of ``source``.
spacing: True to copy the spacing from ``source`` to ``target``, or
the spacing to set in ``target``; defaults to True.
origin: True to copy the origin from ``source`` to ``target``, or
the origin to set in ``target``; defaults to True.
direction: True to copy the direction from ``source`` to ``target``, or
the direction to set in ``target``; defaults to True.
"""
# get the world info from the source if not already set
if spacing is True:
spacing = source.GetSpacing()
if origin is True:
origin = source.GetOrigin()
if direction is True:
direction = source.GetDirection()
# set the values in the target
_logger.debug(
"Adjusting spacing from %s to %s, origin from %s to %s, "
"direction from %s to %s", target.GetSpacing(), spacing,
target.GetOrigin(), origin, target.GetDirection(), direction)
if spacing:
target.SetSpacing(spacing)
if origin:
target.SetOrigin(origin)
if direction:
target.SetDirection(direction)
def set_origin_image(self, origin_img:sitk.Image) -> None :
"""method to set the origin sitk.Image on which we want to resample
Args:
origin_img (sitk.Image): []
"""
self.origin_img = origin_img
self.origin_size = origin_img.GetSize()
self.origin_spacing = origin_img.GetSpacing()
self.origin_direction = origin_img.GetDirection()
self.origin_origin = origin_img.GetOrigin()
def sitk_to_nib(image: sitk.Image) -> Tuple[np.ndarray, np.ndarray]:
data = sitk.GetArrayFromImage(image).transpose()
spacing = np.array(image.GetSpacing())
rotation = np.array(image.GetDirection()).reshape(3, 3)
rotation = np.dot(FLIP_XY, rotation)
rotation_zoom = rotation * spacing
translation = np.dot(FLIP_XY, image.GetOrigin())
affine = np.eye(4)
affine[:3, :3] = rotation_zoom
affine[:3, 3] = translation
return data, affine
def display_info(img: sitk.Image) -> None:
"""display information about a sitk.Image
Args:
img (sitk.Image): [sitk image]
"""
print('img information :')
print('\t Origin :', img.GetOrigin())
print('\t Size :', img.GetSize())
print('\t Spacing :', img.GetSpacing())
print('\t Direction :', img.GetDirection())
def _assert_3d(self, image: sitk.Image, is_vector=False):
self.assertEqual(self.properties_3d.size, image.GetSize())
if is_vector:
self.assertEqual(self.no_vector_components,
image.GetNumberOfComponentsPerPixel())
else:
self.assertEqual(1, image.GetNumberOfComponentsPerPixel())
self.assertEqual(self.origin_spacing_3d, image.GetOrigin())
self.assertEqual(self.origin_spacing_3d, image.GetSpacing())
self.assertEqual(self.direction_3d, image.GetDirection())
def sitk_to_itk(image: sitk.Image) -> Any:
r""" Function to convert an image object from SimpleITK to ITK.
.. note::
Data is copied to the new object (deep copy).
Parameters
----------
image : sitk.Image
Input image.
Returns
-------
any
Image in ITK format.
"""
if 'itk' not in sys.modules:
raise Exception(
'sitk_to_itk: itk module is required to use this feature.')
a = sitk.GetArrayViewFromImage(image)
if len(a.shape) < 4:
result = itk.GetImageFromArray(a)
else:
# NOTE: This workaround is implemented this way since it
# seems that itk.GetImageFromArray() is not working properly
# with vector images.
region = itk.ImageRegion[3]()
region.SetSize(image.GetSize())
region.SetIndex((0, 0, 0))
PixelType = itk.Vector[itk_float_type, 3]
ImageType = itk.Image[PixelType, 3]
# Create new image
result = ImageType.New()
result.SetRegions(region)
result.Allocate()
result.SetSpacing(image.GetSpacing())
# Copy image data
b = itk.GetArrayViewFromImage(result)
b[:] = a[:]
result.SetSpacing(image.GetSpacing())
result.SetOrigin(image.GetOrigin())
return result
def __init__(self, image: sitk.Image):
"""Initializes a new instance of the ImageInformation class.
Args:
image (sitk.Image): The image whose properties to hold.
"""
self.size = image.GetSize()
self.origin = image.GetOrigin()
self.spacing = image.GetSpacing()
self.direction = image.GetDirection()
self.dimensions = image.GetDimension()
self.number_of_components_per_pixel = image.GetNumberOfComponentsPerPixel()
self.pixel_id = image.GetPixelID()
def rgb_to_grayscale_img(image: sitk.Image, white_light_filter_value=0.9):
"""Convert an RGB to grayscale image by extracting the average intensity, filtering out white light >0.9 max"""
array = sitk.GetArrayFromImage(image)
dimension = image.GetDimension()
grayscale_array = np.average(array, 2)
grayscale_array[grayscale_array > white_light_filter_value *
np.max(array)] = 0
grayscale_image = sitk.GetImageFromArray(grayscale_array)
grayscale_image.SetSpacing(image.GetSpacing())
grayscale_image.SetOrigin(image.GetOrigin())
return grayscale_image
def get_mask(ground_truth: sitk.Image,
ground_truth_labels: list,
label_percentages: list,
background_mask: sitk.Image = None) -> sitk.Image:
"""Gets a training mask.
Args:
ground_truth (sitk.Image): The ground truth image.
ground_truth_labels (list of int): The ground truth labels,
where 0=background, 1=label1, 2=label2, ..., e.g. [0, 1]
label_percentages (list of float): The percentage of voxels of a corresponding label to extract as mask,
e.g. [0.2, 0.2].
background_mask (sitk.Image): A mask, where intensity 0 indicates voxels to exclude independent of the label.
Returns:
sitk.Image: The training mask.
"""
# initialize mask
ground_truth_array = sitk.GetArrayFromImage(ground_truth)
mask_array = np.zeros(ground_truth_array.shape, dtype=np.uint8)
# exclude background
if background_mask is not None:
background_mask_array = sitk.GetArrayFromImage(background_mask)
background_mask_array = np.logical_not(background_mask_array)
ground_truth_array = ground_truth_array.astype(
float) # convert to float because of np.nan
ground_truth_array[background_mask_array] = np.nan
for label_idx, label in enumerate(ground_truth_labels):
indices = np.transpose(np.where(ground_truth_array == label))
np.random.shuffle(indices)
no_mask_items = int(indices.shape[0] *
label_percentages[label_idx])
for no in range(no_mask_items):
x = indices[no][0]
y = indices[no][1]
z = indices[no][2]
mask_array[x, y, z] = 1 # this is a masked item
mask = sitk.GetImageFromArray(mask_array)
mask.SetOrigin(ground_truth.GetOrigin())
mask.SetDirection(ground_truth.GetDirection())
mask.SetSpacing(ground_truth.GetSpacing())
return mask
def roi2mask(self, mask_img:sitk.Image, pet_img:sitk.Image) -> sitk.Image:
"""
Generate the thresholded mask from the ROI
Args:
:param mask_img: sitk.Image, raw mask (i.e ROI)
:param pet_img: sitk.Image, the corresponding pet scan
:return: sitk.Image, the ground truth segmentation
"""
# transform to numpy
origin = mask_img.GetOrigin()
spacing = mask_img.GetSpacing()
direction = tuple(mask_img.GetDirection())
mask_array = sitk.GetArrayFromImage(mask_img) #[z,y,x,C]
pet_array = sitk.GetArrayFromImage(pet_img) #[z,y,x]
# get 3D meta information
if len(mask_array.shape) == 3:
mask_array = np.expand_dims(mask_array, axis=0) #[1,z,y,x]
else :
mask_array = np.transpose(mask_array, (3,0,1,2)) #[C,z,y,x]
new_mask = np.zeros(mask_array.shape[1:], dtype=np.int8) #[z,y,x]
for num_slice in range(mask_array.shape[0]):
mask_slice = mask_array[num_slice] #ROI 3D MATRIX
roi = pet_array[mask_slice > 0]
if len(roi) == 0:
# R.O.I is empty
continue
try:
threshold = self.calculate_threshold(roi)
# apply threshold
new_mask[np.where((pet_array >= threshold) & (mask_slice > 0))] = 1
except Exception as e:
print(e)
print(sys.exc_info()[0])
# reconvert to sitk and restore information
new_mask = sitk.GetImageFromArray(new_mask)
new_mask.SetOrigin(origin)
new_mask.SetDirection(direction)
new_mask.SetSpacing(spacing)
return new_mask
def execute(self,
image: sitk.Image,
params: fltr.IFilterParams = None) -> sitk.Image:
"""Executes a atlas coordinates feature extractor on an image.
Args:
image (sitk.Image): The image.
params (fltr.IFilterParams): The parameters (unused).
Returns:
sitk.Image: The atlas coordinates image
(a vector image with 3 components, which represent the physical x, y, z coordinates in mm).
Raises:
ValueError: If image is not 3-D.
"""
if image.GetDimension() != 3:
raise ValueError('image needs to be 3-D')
x, y, z = image.GetSize()
# create matrix with homogenous indices in axis 3
coords = np.zeros((x, y, z, 4))
coords[..., 0] = np.arange(x)[:, np.newaxis, np.newaxis]
coords[..., 1] = np.arange(y)[np.newaxis, :, np.newaxis]
coords[..., 2] = np.arange(z)[np.newaxis, np.newaxis, :]
coords[..., 3] = 1
# reshape such that each voxel is one row
lin_coords = np.reshape(
coords, [coords.shape[0] * coords.shape[1] * coords.shape[2], 4])
# generate transformation matrix
tmpmat = image.GetDirection() + image.GetOrigin()
tfm = np.reshape(tmpmat, [3, 4], order='F')
tfm = np.vstack((tfm, [0, 0, 0, 1]))
atlas_coords = (tfm @ np.transpose(lin_coords))[0:3, :]
atlas_coords = np.reshape(np.transpose(atlas_coords), [z, y, x, 3],
'F')
img_out = sitk.GetImageFromArray(atlas_coords)
img_out.CopyInformation(image)
return img_out
def roi2mask(self, mask_img:sitk.Image, pet_img:sitk.Image) -> sitk.Image:
"""
Generate the thresholded mask from the ROI with otsu, 41%, 2.5 and 4.0 segmentation
Args:
:param mask_img: sitk.Image, raw mask (i.e ROI)
:param pet_img: sitk.Image, the corresponding pet scan
:return: sitk.Image, the ground truth segmentation
"""
# transform to numpy
origin = mask_img.GetOrigin()
spacing = mask_img.GetSpacing()
direction = tuple(mask_img.GetDirection())
mask_array = sitk.GetArrayFromImage(mask_img)
pet_array = sitk.GetArrayFromImage(pet_img)
# get 3D meta information
if len(mask_array.shape) == 3:
mask_array = np.expand_dims(mask_array, axis=0)
else:
mask_array = np.transpose(mask_array, (3,0,1,2))
new_masks = []
#otsu
#print('otsu')
new_masks.append(self.__roi_seg(mask_array, pet_array, threshold='otsu'))
#print('41%')
new_masks.append(self.__roi_seg(mask_array, pet_array, threshold='0.41'))
#2.5
#print('2.5')
new_masks.append(self.__roi_seg(mask_array, pet_array, threshold='2.5'))
#4.0
#print('4.0')
new_masks.append(self.__roi_seg(mask_array, pet_array, threshold='4.0'))
new_mask = np.stack(new_masks, axis=3)
new_mask = np.mean(new_mask, axis=3)
# reconvert to sitk and restore 3D meta-information
new_mask = sitk.GetImageFromArray(new_mask)
new_mask.SetOrigin(origin)
new_mask.SetDirection(direction)
new_mask.SetSpacing(spacing)
return new_mask
def __call__(self, img: sitk.Image) -> sitk.Image:
size, spacing, origin, direction = img.GetSize(), img.GetSpacing(), img.GetOrigin(), img.GetDirection()
scale = [ns / s for ns, s in zip(self.new_spacing, spacing)]
new_size = [int(sz/sc) for sz, sc in zip(size, scale)]
# new_origin = [o / sc for o, sc in zip(origin, scale)]
resampler = sitk.ResampleImageFilter()
resampler.SetSize(new_size)
# resampler.SetTransform(sitk.Transform())
resampler.SetInterpolator(sitk.sitkLinear)
resampler.SetOutputDirection(direction)
# resampler.SetOutputOrigin(new_origin) # misfitted image when using adapted origin
resampler.SetOutputOrigin(origin)
resampler.SetOutputSpacing(self.new_spacing)
return resampler.Execute(img)
def scale_image(image: sitk.Image,
new_size: Tuple[int, ...],
interpolator: int = sitk.sitkLinear) -> sitk.Image:
r""" Scale an image in a grid of given size.
Parameters
----------
image : sitk.Image
An input image.
new_size : Tuple[int, ...]
A tuple of integers expressing the new size.
interpolator : int
A SimpleITK interpolator enum value.
Returns
-------
sitk.Image
Resized image.
"""
if type(image) is not sitk.SimpleITK.Image:
raise Exception("unsupported image object type")
if type(new_size) != type((1, 1)):
raise Exception("new_size must be a tuple of integers")
size = image.GetSize()
if len(new_size) != len(size):
raise Exception("new_size must match the image dimensionality")
spacing = []
for s, x, nx in zip(image.GetSpacing(), size, new_size):
spacing.append(s * x / nx)
resampler = sitk.ResampleImageFilter()
resampler.SetSize(new_size)
resampler.SetOutputSpacing(tuple(spacing))
resampler.SetOutputOrigin(image.GetOrigin())
resampler.SetOutputDirection(image.GetDirection())
resampler.SetInterpolator(interpolator)
# Anti-aliasing
image = sitk.SmoothingRecursiveGaussian(image, 2.0)
return resampler.Execute(image)
def copy_meta_data_itk(source: sitk.Image, target: sitk.Image) -> sitk.Image:
"""
Copy meta data between files
Args:
source: source file
target: target file
Returns:
sitk.Image: target file with copied meta data
"""
# for i in source.GetMetaDataKeys():
# target.SetMetaData(i, source.GetMetaData(i))
raise NotImplementedError("Does not work!")
target.SetOrigin(source.GetOrigin())
target.SetDirection(source.GetDirection())
target.SetSpacing(source.GetSpacing())
return target
def compute_dice(b1: sitk.Image, b2: sitk.Image):
b2 = sitk.Resample(
b2,
b1.GetSize(),
sitk.Transform(),
sitk.sitkNearestNeighbor,
b1.GetOrigin(),
b1.GetSpacing(),
b1.GetDirection(),
0,
b2.GetPixelID(),
)
labstats = sitk.LabelOverlapMeasuresImageFilter()
labstats.Execute(b1, b2)
return labstats.GetDiceCoefficient()
def remove_small_roi(cls, binary_img: sitk.Image,
pet_img: sitk.Image) -> sitk.Image:
"""function to remove ROI under 30 ml on a binary sitk.Image
Args:
binary_img (sitk.Image): [sitk.Image of size (z,y,x)]
pet_img (sitk.Image): [sitk.Image of the PET, size (z,y,x)]
Raises:
Exception: [raise Exception if not a 3D binary mask]
Returns:
[sitk.Image]: [Return cleaned image]
"""
binary_array = sitk.GetArrayFromImage(binary_img)
if len(binary_array.shape) != 3 or int(np.max(binary_array)) != 1:
raise Exception(
"Not a 3D binary mask, need to transform into 3D binary mask")
else:
pet_spacing = pet_img.GetSpacing()
pet_origin = pet_img.GetOrigin()
pet_direction = pet_img.GetDirection()
labelled_img = sitk.ConnectedComponent(binary_img)
stats = sitk.LabelIntensityStatisticsImageFilter()
stats.Execute(labelled_img, pet_img)
labelled_array = sitk.GetArrayFromImage(labelled_img).transpose()
number_of_label = stats.GetNumberOfLabels()
volume_voxel = pet_spacing[0] * pet_spacing[1] * pet_spacing[
2] * 10**(-3) #in ml
for i in range(1, number_of_label + 1):
volume_roi = stats.GetNumberOfPixels(i) * volume_voxel
if volume_roi < float(30):
x, y, z = np.where(labelled_array == i)
for j in range(len(x)):
labelled_array[x[j], y[j], z[j]] = 0
new_binary_array = np.zeros((labelled_array.shape))
new_binary_array[np.where(labelled_array != 0)] = 1
new_binary_img = sitk.GetImageFromArray(
new_binary_array.transpose().astype(np.uint8))
new_binary_img.SetOrigin(pet_origin)
new_binary_img.SetSpacing(pet_spacing)
new_binary_img.SetDirection(pet_direction)
return new_binary_img
def sitk_to_nib(image: sitk.Image) -> Tuple[np.ndarray, np.ndarray]:
data = sitk.GetArrayFromImage(image).transpose()
spacing = np.array(image.GetSpacing())
direction = np.array(image.GetDirection())
origin = image.GetOrigin()
if len(direction) == 9:
rotation = direction.reshape(3, 3)
elif len(direction) == 4: # ignore first dimension if 2D (1, 1, H, W)
rotation_2d = direction.reshape(2, 2)
rotation = np.eye(3)
rotation[1:3, 1:3] = rotation_2d
spacing = 1, *spacing
origin = 0, *origin
rotation = np.dot(FLIP_XY, rotation)
rotation_zoom = rotation * spacing
translation = np.dot(FLIP_XY, origin)
affine = np.eye(4)
affine[:3, :3] = rotation_zoom
affine[:3, 3] = translation
return data, affine
def sitk_copy_metadata(img_source: sitk.Image, img_target: sitk.Image) -> sitk.Image:
"""
Copy metadata (spacing, origin, direction) from source to target image
Args
img_source: source image
img_target: target image
Returns:
SimpleITK.Image: target image with copied metadata
"""
raise RuntimeError("Deprecated")
spacing = img_source.GetSpacing()
img_target.SetSpacing(spacing)
origin = img_source.GetOrigin()
img_target.SetOrigin(origin)
direction = img_source.GetDirection()
img_target.SetDirection(direction)
return img_target
