catmssim_3d_cube: Multiscale Categorical Structural Similarity Index Measure... In gzt/catsim: Binary and Categorical Image Structural Similarity Metric

Description

The categorical structural similary 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 4x4x4 windows by default with 5 levels of downsampling.

Usage

 1 2 catmssim_3d_cube(x, y, weights = c(0.0448, 0.2856, 0.3001, 0.2363, 0.1333), window = 5, method = "Cohen", ...)

Arguments

 x a binary or categorical image y a binary or categorical image weights a vector of weights for the different scales. By default, five different scales are used. window size of window, by default 5 method whether to use Cohen's kappa, Jaccard Index, or Adjusted Rand Index as the similarity index. Note Jaccard should only be used on binary data. ... additional constants can be passed to internal functions.

Value

a value less than 1 indicating the similarity between the images.

Examples

 1 2 3 4 5 6 7 8 9 10 11 12 13 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 = "Jaccard") # And using the last possible similarity score catmssim_3d_cube(x,y, weights = c(.75,.25), method = "Rand")

gzt/catsim documentation built on July 28, 2019, 10:36 p.m.