Calculates similarity between two 3D volumes. Both volumes have to be in the same coordinate system, same space within that coordinate system and with the same voxel dimensions.
>>> from nipype.interfaces.nipy.utils import Similarity
>>> similarity = Similarity()
>>> similarity.inputs.volume1 = 'rc1s1.nii'
>>> similarity.inputs.volume2 = 'rc1s2.nii'
>>> similarity.inputs.mask1 = 'mask.nii'
>>> similarity.inputs.mask2 = 'mask.nii'
>>> similarity.inputs.metric = 'cr'
>>> res = similarity.run()
Inputs:
[Mandatory]
volume1: (an existing file name)
3D volume
volume2: (an existing file name)
3D volume
[Optional]
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
mask1: (an existing file name)
3D volume
mask2: (an existing file name)
3D volume
metric
str or callable
Cost-function for assessing image similarity. If a string,
one of 'cc': correlation coefficient, 'cr': correlation
ratio, 'crl1': L1-norm based correlation ratio, 'mi': mutual
information, 'nmi': normalized mutual information, 'slr':
supervised log-likelihood ratio. If a callable, it should
take a two-dimensional array representing the image joint
histogram as an input and return a float.
Outputs:
similarity: (a float)
Similarity between volume 1 and 2