# 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.