The categorical structural similarity index measure for 2D categorical or binary images for multiple scales. The default is to compute over 5 scales.
catmssim_2d( x, y, levels = NULL, weights = NULL, window = 11, method = "Cohen", ..., random = "random" )
x, y | a binary or categorical image |
---|---|
levels | how many levels of downsampling to use. By default, 5. If
|
weights | a vector of weights for the different scales. By default,
equal to |
window | by default 11 for 2D and 5 for 3D images,
but can be specified as a
vector if the window sizes differ by dimension.
The vector must have the same number of
dimensions as the inputted |
method | whether to use Cohen's kappa ( |
... | additional constants can be passed to internal functions. |
random | whether to have deterministic PRNG ( |
a value less than 1 indicating the similarity between the images.
set.seed(20181207) x <- matrix(sample(0:3, 128^2, replace = TRUE), nrow = 128) y <- x for (i in 1:128) y[i, i] <- 0 for (i in 1:127) y[i, i + 1] <- 0 catmssim_2d(x, y, method = "Cohen", levels = 2) # the default#> [1] 0.8702514# now using a different similarity score (Jaccard Index) catmssim_2d(x, y, method = "NMI")#> Warning: Truncating levels because of minimum dimension.#> [1] 0.4764754