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# algorithms.metrics

## Distance

Link to code

Calculates distance between two volumes.

Inputs:

    [Mandatory]
    volume1: (an existing file name)
            Has to have the same dimensions as volume2.
    volume2: (an existing file name)
            Has to have the same dimensions as volume1.

    [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
    mask_volume: (an existing file name)
            calculate overlap only within this mask.
    method: ('eucl_min' or 'eucl_cog' or 'eucl_mean' or 'eucl_wmean' or
             'eucl_max', nipype default value: eucl_min)
            ""eucl_min": Euclidean distance between two closest points
            "eucl_cog": mean Euclidian distance between the Center of Gravity of
            volume1 and CoGs of volume2 "eucl_mean": mean Euclidian minimum
            distance of all volume2 voxels to volume1 "eucl_wmean": mean
            Euclidian minimum distance of all volume2 voxels to volume1 weighted
            by their values "eucl_max": maximum over minimum Euclidian distances
            of all volume2 voxels to volume1 (also known as the Hausdorff
            distance)

Outputs:

    distance: (a float)
    histogram: (a file name)
    point1: (an array with shape (3,))
    point2: (an array with shape (3,))

## ErrorMap

Link to code

Calculates the error (distance) map between two input volumes.

### Example

    >>> errormap = ErrorMap()
    >>> errormap.inputs.in_ref = 'cont1.nii'
    >>> errormap.inputs.in_tst = 'cont2.nii'
    >>> res = errormap.run()

Inputs:

    [Mandatory]
    in_ref: (an existing file name)
            Reference image. Requires the same dimensions as in_tst.
    in_tst: (an existing file name)
            Test image. Requires the same dimensions as in_ref.
    metric: ('sqeuclidean' or 'euclidean', nipype default value:
             sqeuclidean)
            error map metric (as implemented in scipy cdist)

    [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
    mask: (an existing file name)
            calculate overlap only within this mask.
    out_map: (a file name)
            Name for the output file

Outputs:

    out_map: (an existing file name)
            resulting error map

## FuzzyOverlap

Link to code

Calculates various overlap measures between two maps, using the fuzzy definition proposed in: Crum et al., Generalized Overlap Measures for Evaluation and Validation in Medical Image Analysis, IEEE Trans. Med. Ima. 25(11),pp 1451-1461, Nov. 2006.

in_ref and in_tst are lists of 2/3D images, each element on the list containing one volume fraction map of a class in a fuzzy partition of the domain.

### Example

    >>> overlap = FuzzyOverlap()
    >>> overlap.inputs.in_ref = [ 'ref_class0.nii', 'ref_class1.nii' ]
    >>> overlap.inputs.in_tst = [ 'tst_class0.nii', 'tst_class1.nii' ]
    >>> overlap.inputs.weighting = 'volume'
    >>> res = overlap.run()

Inputs:

    [Mandatory]
    in_ref: (an existing file name)
            Reference image. Requires the same dimensions as in_tst.
    in_tst: (an existing file name)
            Test image. Requires the same dimensions as in_ref.

    [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
    out_file: (a file name, nipype default value: diff.nii)
            alternative name for resulting difference-map
    weighting: ('none' or 'volume' or 'squared_vol', nipype default
             value: none)
            'none': no class-overlap weighting is performed. 'volume': computed
            class-overlaps are weighted by class volume 'squared_vol': computed
            class-overlaps are weighted by the squared volume of the class

Outputs:

    class_fdi: (a list of items which are a float)
            Array containing the fDIs of each computed class
    class_fji: (a list of items which are a float)
            Array containing the fJIs of each computed class
    dice: (a float)
            Fuzzy Dice Index (fDI), all the classes
    diff_file: (an existing file name)
            resulting difference-map of all classes, using the chosen weighting
    jaccard: (a float)
            Fuzzy Jaccard Index (fJI), all the classes

## Overlap

Link to code

Calculates Dice and Jaccard’s overlap measures between two ROI maps. The interface is backwards compatible with the former version in which only binary files were accepted.

The averaged values of overlap indices can be weighted. Volumes now can be reported in , although they are given in voxels to keep backwards compatibility.

### Example

    >>> overlap = Overlap()
    >>> overlap.inputs.volume1 = 'cont1.nii'
    >>> overlap.inputs.volume2 = 'cont2.nii'
    >>> res = overlap.run()

Inputs:

    [Mandatory]
    bg_overlap: (a boolean, nipype default value: False)
            consider zeros as a label
    vol_units: ('voxel' or 'mm', nipype default value: voxel)
            units for volumes
    volume1: (an existing file name)
            Has to have the same dimensions as volume2.
    volume2: (an existing file name)
            Has to have the same dimensions as volume1.

    [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
    mask_volume: (an existing file name)
            calculate overlap only within this mask.
    out_file: (a file name, nipype default value: diff.nii)
    weighting: ('none' or 'volume' or 'squared_vol', nipype default
             value: none)
            'none': no class-overlap weighting is performed. 'volume': computed
            class-overlaps are weighted by class volume 'squared_vol': computed
            class-overlaps are weighted by the squared volume of the class

Outputs:

    dice: (a float)
            averaged dice index
    diff_file: (an existing file name)
            error map of differences
    jaccard: (a float)
            averaged jaccard index
    labels: (a list of items which are an integer)
            detected labels
    roi_di: (a list of items which are a float)
            the Dice index (DI) per ROI
    roi_ji: (a list of items which are a float)
            the Jaccard index (JI) per ROI
    roi_voldiff: (a list of items which are a float)
            volume differences of ROIs
    volume_difference: (a float)
            averaged volume difference

## Similarity

Link to code

Calculates similarity between two 3D or 4D volumes. Both volumes have to be in the same coordinate system, same space within that coordinate system and with the same voxel dimensions.

Note

This interface is an extension of `nipype.interfaces.nipy.utils.Similarity` to support 4D files. Requires `nipy`

### Example

    >>> from nipype.algorithms.metrics 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/4D volume
    volume2: (an existing file name)
            3D/4D 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 list of items which are a float)