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# medpy.filter.image.sls¶

medpy.filter.image.sls(minuend, subtrahend, metric='ssd', noise='global', signed=True, sn_size=None, sn_footprint=None, sn_mode='reflect', sn_cval=0.0, pn_size=None, pn_footprint=None, pn_mode='reflect', pn_cval=0.0)[source]

Computes the signed local similarity between two images.

Compares a patch around each voxel of the minuend array to a number of patches centered at the points of a search neighbourhood in the subtrahend. Thus, creates a multi-dimensional measure of patch similarity between the minuend and a corresponding search area in the subtrahend.

This filter can also be used to compute local self-similarity, obtaining a descriptor similar to the one described in [R7].

Parameters: minuend : array_like Input array from which to subtract the subtrahend. subtrahend : array_like Input array to subtract from the minuend. metric : {‘ssd’, ‘mi’, ‘nmi’, ‘ncc’}, optional The metric parameter determines the metric used to compute the filter output. Default is ‘ssd’. noise : {‘global’, ‘local’}, optional The noise parameter determines how the noise is handled. If set to ‘global’, the variance determining the noise is a scalar, if set to ‘local’, it is a Gaussian smoothed field of estimated local noise. Default is ‘global’. signed : bool, optional Whether the filter output should be signed or not. If set to ‘False’, only the absolute values will be returned. Default is ‘True’. sn_size : scalar or tuple, optional See sn_footprint, below sn_footprint : array, optional The search neighbourhood. Either sn_size or sn_footprint must be defined. sn_size gives the shape that is taken from the input array, at every element position, to define the input to the filter function. sn_footprint is a boolean array that specifies (implicitly) a shape, but also which of the elements within this shape will get passed to the filter function. Thus sn_size=(n,m) is equivalent to sn_footprint=np.ones((n,m)). We adjust sn_size to the number of dimensions of the input array, so that, if the input array is shape (10,10,10), and sn_size is 2, then the actual size used is (2,2,2). sn_mode : {‘reflect’, ‘constant’, ‘nearest’, ‘mirror’, ‘wrap’}, optional The sn_mode parameter determines how the array borders are handled, where sn_cval is the value when mode is equal to ‘constant’. Default is ‘reflect’ sn_cval : scalar, optional Value to fill past edges of input if sn_mode is ‘constant’. Default is 0.0 pn_size : scalar or tuple, optional See pn_footprint, below pn_footprint : array, optional The patch over which the distance measure is applied. Either pn_size or pn_footprint must be defined. pn_size gives the shape that is taken from the input array, at every element position, to define the input to the filter function. pn_footprint is a boolean array that specifies (implicitly) a shape, but also which of the elements within this shape will get passed to the filter function. Thus pn_size=(n,m) is equivalent of dimensions of the input array, so that, if the input array is shape (10,10,10), and pn_size is 2, then the actual size used is (2,2,2). pn_mode : {‘reflect’, ‘constant’, ‘nearest’, ‘mirror’, ‘wrap’}, optional The pn_mode parameter determines how the array borders are handled, where pn_cval is the value when mode is equal to ‘constant’. Default is ‘reflect’ pn_cval : scalar, optional Value to fill past edges of input if pn_mode is ‘constant’. Default is 0.0 sls : ndarray The signed local similarity image between subtrahend and minuend.

References

 [R7] (1, 2) Mattias P. Heinrich, Mark Jenkinson, Manav Bhushan, Tahreema Matin, Fergus V. Gleeson, Sir Michael Brady, Julia A. Schnabel MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration Medical Image Analysis, Volume 16, Issue 7, October 2012, Pages 1423-1435, ISSN 1361-8415 http://dx.doi.org/10.1016/j.media.2012.05.008