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medpy.filter.image.ssd(minuend, subtrahend, normalized=True, signed=False, size=None, footprint=None, mode='reflect', cval=0.0, origin=0)[source]
Parameters: minuend : array_like Input array from which to subtract the subtrahend. subtrahend : array_like Input array to subtract from the minuend. normalized : bool, optional Whether the SSD of each patch should be divided through the filter size for normalization. Default is ‘True’. signed : bool, optional Whether the accumulative sign of each patch should be returned as well. If ‘True’, the second return value is a numpy.sign array, otherwise the scalar ‘1’. Default is ‘False’. size : scalar or tuple, optional See footprint, below footprint : array, optional The patch over which to compute the SSD. Either size or footprint must be defined. size gives the shape that is taken from the input array, at every element position, to define the input to the filter function. 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 size=(n,m) is equivalent to footprint=np.ones((n,m)). We adjust size to the number of dimensions of the input array, so that, if the input array is shape (10,10,10), and size is 2, then the actual size used is (2,2,2). mode : {‘reflect’, ‘constant’, ‘nearest’, ‘mirror’, ‘wrap’}, optional The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to ‘constant’. Default is ‘reflect’ cval : scalar, optional Value to fill past edges of input if mode is ‘constant’. Default is 0.0 ssd : ndarray The patchwise sum of squared differences between minuend and subtrahend.