medpy.metric.binary.obj_asd¶

medpy.metric.binary.
obj_asd
(result, reference, voxelspacing=None, connectivity=1)[source]¶ Average surface distance between objects.
First correspondences between distinct binary objects in reference and result are established. Then the average surface distance is only computed between corresponding objects. Correspondence is defined as unique and at least one voxel overlap.
Parameters: result : array_like
Input data containing objects. Can be any type but will be converted into binary: background where 0, object everywhere else.
reference : array_like
Input data containing objects. Can be any type but will be converted into binary: background where 0, object everywhere else.
voxelspacing : float or sequence of floats, optional
The voxelspacing in a distance unit i.e. spacing of elements along each dimension. If a sequence, must be of length equal to the input rank; if a single number, this is used for all axes. If not specified, a grid spacing of unity is implied.
connectivity : int
The neighbourhood/connectivity considered when determining what accounts for a distinct binary object as well as when determining the surface of the binary objects. This value is passed to
scipy.ndimage.morphology.generate_binary_structure
and should usually be \(> 1\). The decision on the connectivity is important, as it can influence the results strongly. If in doubt, leave it as it is.Returns: asd : float
The average surface distance between all mutually existing distinct binary object(s) in
result
andreference
. The distance unit is the same as for the spacing of elements along each dimension, which is usually given in mm.Notes
This is not a real metric, as it is directed. See
obj_assd
for a real metric of this.For the understanding of this metric, both the notions of connectedness and surface distance are essential. Please see
obj_tpr
andobj_fpr
for more information on the first andasd
on the second.Examples
>>> arr1 = numpy.asarray([[1,1,1],[1,1,1],[1,1,1]]) >>> arr2 = numpy.asarray([[0,1,0],[0,1,0],[0,1,0]]) >>> arr1 array([[1, 1, 1], [1, 1, 1], [1, 1, 1]]) >>> arr2 array([[0, 1, 0], [0, 1, 0], [0, 1, 0]]) >>> obj_asd(arr1, arr2) 1.5 >>> obj_asd(arr2, arr1) 0.333333333333
With the voxelspacing parameter, the distances between the voxels can be set for each dimension separately:
>>> obj_asd(arr1, arr2, voxelspacing=(1,2)) 1.5 >>> obj_asd(arr2, arr1, voxelspacing=(1,2)) 0.333333333333
More examples depicting the notion of object connectedness:
>>> arr1 = numpy.asarray([[1,0,1],[1,0,0],[0,0,0]]) >>> arr2 = numpy.asarray([[1,0,1],[1,0,0],[0,0,1]]) >>> arr1 array([[1, 0, 1], [1, 0, 0], [0, 0, 0]]) >>> arr2 array([[1, 0, 1], [1, 0, 0], [0, 0, 1]]) >>> obj_asd(arr1, arr2) 0.0 >>> obj_asd(arr2, arr1) 0.0
>>> arr1 = numpy.asarray([[1,0,1],[1,0,1],[0,0,1]]) >>> arr2 = numpy.asarray([[1,0,1],[1,0,0],[0,0,1]]) >>> arr1 array([[1, 0, 1], [1, 0, 1], [0, 0, 1]]) >>> arr2 array([[1, 0, 1], [1, 0, 0], [0, 0, 1]]) >>> obj_asd(arr1, arr2) 0.6 >>> obj_asd(arr2, arr1) 0.0
Influence of connectivity parameter can be seen in the following example, where with the (default) connectivity of 1 the first array is considered to contain two objects, while with an increase connectivity of 2, just one large object is detected.
>>> arr1 = numpy.asarray([[1,0,0],[0,1,1],[0,1,1]]) >>> arr2 = numpy.asarray([[1,0,0],[0,0,0],[0,0,0]]) >>> arr1 array([[1, 0, 0], [0, 1, 1], [0, 1, 1]]) >>> arr2 array([[1, 0, 0], [0, 0, 0], [0, 0, 0]]) >>> obj_asd(arr1, arr2) 0.0 >>> obj_asd(arr1, arr2, connectivity=2) 1.742955328
Note that the connectivity also influence the notion of what is considered an object surface voxels.