interfaces.nipy.utils

Similarity

Link to code

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() 

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: (u'cc' or u'cr' or u'crl1' or u'mi' or u'nmi' or u'slr' or a
         callable value, nipype default value: None)
        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