Description Usage Arguments Value Examples
The categorical structural similarity index measure for 3D categorical or binary images for multiple scales. The default is to compute over 5 scales. This computes a 3D measure based on 5x5x5 windows by default with 5 levels of downsampling.
1 2 3 4 5 6 7 8 9 10 |
x |
a binary or categorical image |
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.
1 2 3 4 5 6 7 8 9 10 11 | set.seed(20181207)
dim <- 16
x <- array(sample(0:4, dim^3, replace = TRUE), dim = c(dim, dim, dim))
y <- x
for (j in 1:dim) {
for (i in 1:dim) y[i, i, j] <- 0
for (i in 1:(dim - 1)) y[i, i + 1, j] <- 0
}
catmssim_3d_cube(x, y, weights = c(.75, .25))
# Now using a different similarity score
catmssim_3d_cube(x, y, weights = c(.75, .25), method = "Accuracy")
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