def test_image_properties(self):
dut = pymia_fltr_prep.NormalizeZScore()
out = dut.execute(self.image)
self.assertEqual(self.image.GetSize(), out.GetSize())
self.assertEqual(self.image.GetOrigin(), out.GetOrigin())
self.assertEqual(self.image.GetSpacing(), out.GetSpacing())
self.assertEqual(self.image.GetDirection(), out.GetDirection())
self.assertEqual(self.image.GetDimension(), out.GetDimension())
self.assertEqual(self.image.GetNumberOfComponentsPerPixel(), out.GetNumberOfComponentsPerPixel())
self.assertEqual(sitk.sitkFloat64, out.GetPixelID())
def pre_process(id_: str, paths: dict, **kwargs) -> structure.BrainImage:
"""Loads and processes an image.
The processing includes:
- Registration
- Pre-processing
- Feature extraction
Args:
id_ (str): An image identifier.
paths (dict): A dict, where the keys are an image identifier of type structure.BrainImageTypes
and the values are paths to the images.
Returns:
(structure.BrainImage):
"""
print('-' * 10, 'Processing', id_)
# load image
path = paths.pop(
id_, '') # the value with key id_ is the root directory of the image
img = {img_key: sitk.ReadImage(path) for img_key, path in paths.items()}
img = structure.BrainImage(id_, path, img)
# construct T1 pipeline
pipeline_t1 = fltr.FilterPipeline()
if kwargs.get('zscore_pre', False):
pipeline_t1.add_filter(fltr_prep.NormalizeZScore())
if kwargs.get('registration_pre', False):
pipeline_t1.add_filter(fltr_reg.MultiModalRegistration())
pipeline_t1.set_param(fltr_reg.MultiModalRegistrationParams(atlas_t1),
1)
# execute pipeline on T1 image
img.images[structure.BrainImageTypes.T1] = pipeline_t1.execute(
img.images[structure.BrainImageTypes.T1])
# construct T2 pipeline
pipeline_t2 = fltr.FilterPipeline()
if kwargs.get('zscore_pre', False):
pipeline_t2.add_filter(fltr_prep.NormalizeZScore())
# execute pipeline on T2 image
img.images[structure.BrainImageTypes.T2] = pipeline_t2.execute(
img.images[structure.BrainImageTypes.T2])
if kwargs.get('registration_pre', False):
# get transformation
transform = pipeline_t1.filters[1].transform
# apply transformation of T1 image registration to T2 image
image_t2 = img.images[structure.BrainImageTypes.T2]
image_t2 = sitk.Resample(image_t2, atlas_t1, transform,
sitk.sitkLinear, 0.0,
image_t2.GetPixelIDValue())
img.images[structure.BrainImageTypes.T2] = image_t2
# apply transformation of T1 image registration to ground truth
image_ground_truth = img.images[structure.BrainImageTypes.GroundTruth]
image_ground_truth = sitk.Resample(
image_ground_truth, atlas_t1, transform, sitk.sitkNearestNeighbor,
0, image_ground_truth.GetPixelIDValue())
img.images[structure.BrainImageTypes.GroundTruth] = image_ground_truth
# update image properties to atlas image properties after registration
img.image_properties = conversion.ImageProperties(atlas_t1)
# extract the features
feature_extractor = FeatureExtractor(img, **kwargs)
img = feature_extractor.execute()
img.feature_images = {}
return img
def test_normalization_with_param(self):
dut = pymia_fltr_prep.NormalizeZScore()
out = dut.execute(self.image, pymia_fltr.IFilterParams())
out_arr = sitk.GetArrayFromImage(out)
np.testing.assert_array_almost_equal(self.desired, out_arr, decimal=12)
