FLC

class FLC(target, template, template_mask=None, target_mask=None, pad_target_edges=False, pad_fourier=False, rotate_mask=True, interpolation_order=1, negate_score=True, **kwargs)

Bases: _MatchDensityToDensity

Computes a normalized cross-correlation score of a target f a template g and a mask m:

\[\frac{CC(f, \frac{g*m - \overline{g*m}}{\sigma_{g*m}})} {N_m * \sqrt{ \frac{CC(f^2, m)}{N_m} - (\frac{CC(f, m)}{N_m})^2} }\]

Where:

\[CC(f,g) = \mathcal{F}^{-1}(\mathcal{F}(f) \cdot \mathcal{F}(g)^*)\]

and Nm is the number of voxels within the template mask m.

Parameters:

targetarray_like

The target density array.

templatearray_like

The template density array.

template_maskarray_like, optional

Mask array for the template density.

target_maskarray_like, optional

Mask array for the target density.

pad_target_edgesbool, optional

Whether to pad the edges of the target density array. Default is False.

pad_fourierbool, optional

Whether to pad the Fourier transform of the target and template densities.

rotate_maskbool, optional

Whether to rotate the mask arrays along with the densities. Default is True.

interpolation_orderint, optional

The interpolation order for rigid transforms. Default is 1.

negate_scorebool, optional

Whether the final score should be multiplied by negative one. Default is True.

**kwargsDict, optional

Keyword arguments propagated to downstream functions.

Methods

FLC.rotate_array(arr, rotation_matrix, ...)
FLC.score(x)	Compute the matching score for the given transformation parameters.
FLC.score_angles(x)	Computes the score after a given rotation.
FLC.score_translation(x)	Computes the score after a given translation.