def mi_img(y_true, y_pred, mask=None, metric_type="MattesMutualInformation"):
"""Compute the mutual information (MI) between two images.
Parameters
----------
y_true : np.array
Image 1. Either (h, w) of (N, h, w). If (N, h, w), the decorator `multiple_images_decorator` takes care of
the sample dimension.
y_pred : np.array
Image 2. Either (h, w) of (N, h, w). If (N, h, w), the decorator `multiple_images_decorator` takes care of
the sample dimension.
mask: np.array, optional
Optional, can be specified to have the computation carried out on a precise area.
metric_type: str, {'MattesMutualInformation', 'JointHistogramMutualInformation'}
Type of mutual information computation.
Returns
-------
mi : float
The mutual information (MI) metric. Similarity metric, the higher the more similar the images are.
"""
y_true_ants = ants.image_clone(ants.from_numpy(y_true), pixeltype="float")
y_pred_ants = ants.image_clone(ants.from_numpy(y_pred), pixeltype="float")
if mask is None:
mi = ants.image_similarity(y_true_ants,
y_pred_ants,
metric_type=metric_type)
else:
mask_ants = ants.image_clone(ants.from_numpy(mask.astype(float)),
pixeltype="float")
mi = ants.image_similarity(
y_true_ants,
y_pred_ants,
fixed_mask=mask_ants,
moving_mask=mask_ants,
metric_type=metric_type,
)
return -mi
def cross_correlation_img(y_true, y_pred, mask=None):
"""Compute the cross correlation metric between two images.
Parameters
----------
y_true : np.array
Image 1. Either (h, w) of (N, h, w). If (N, h, w), the decorator `multiple_images_decorator` takes care of
the sample dimension.
y_pred : np.array
Image 2. Either (h, w) of (N, h, w). If (N, h, w), the decorator `multiple_images_decorator` takes care of
the sample dimension.
mask: np.array, optional
Optional, can be specified to have the computation carried out on a precise area.
Returns
-------
cc : float
The Cross-Correlation value. Similarity metric, the higher the more similar the images are.
"""
y_true_ants = ants.image_clone(ants.from_numpy(y_true), pixeltype="float")
y_pred_ants = ants.image_clone(ants.from_numpy(y_pred), pixeltype="float")
if mask is None:
cc = ants.image_similarity(y_true_ants,
y_pred_ants,
metric_type="Correlation")
else:
mask_ants = ants.image_clone(ants.from_numpy(mask.astype(float)),
pixeltype="float")
cc = ants.image_similarity(
y_true_ants,
y_pred_ants,
fixed_mask=mask_ants,
moving_mask=mask_ants,
metric_type="Correlation",
)
return -cc
def test_image_similarity_example(self):
x = ants.image_read(ants.get_ants_data('r16'))
y = ants.image_read(ants.get_ants_data('r30'))
metric = ants.image_similarity(x, y, metric_type='MeanSquares')
def test_image_similarity_example(self):
x = ants.image_read(ants.get_ants_data("r16"))
y = ants.image_read(ants.get_ants_data("r30"))
metric = ants.image_similarity(x, y, metric_type="MeanSquares")