nipype.interfaces.nipy.utils module¶
Similarity¶
Bases: NipyBaseInterface
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.
Deprecated since version 0.10.0: Use
nipype.algorithms.metrics.Similarity
instead.Example
>>> 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()
- volume1a pathlike object or string representing an existing file
3D volume.
- volume2a pathlike object or string representing an existing file
3D volume.
- mask1a pathlike object or string representing an existing file
3D volume.
- mask2a pathlike object or string representing an existing file
3D volume.
- metric‘cc’ or ‘cr’ or ‘crl1’ or ‘mi’ or ‘nmi’ or ‘slr’ or a callable value
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. (Nipype default value:
None
)
- similaritya float
Similarity between volume 1 and 2.