Description Usage Arguments Value Examples

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.

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

`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. |

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

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.

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