The goal of catsim is to provide a similarity measure for binary or
categorical images in either 2D or 3D similar to the MS-SSIM
index for color
images. Suppose you have a ground truth segmentation of some image that
has been segmented into regions - perhaps a brain scan with different
types of tissues or a map with different types of terrain - and a
segmentation produced by some classification method. Comparing the two
pixel-by pixel (or voxel-by-voxel) might work well, but a method that
captures structural similarities might work better for your purposes.
MS-SSIM is an image comparison metric that tries to match the assessment
of the human visual system by considering structural similarities across
multiple scales. CatSIM applies a similar logic in the case of 2-D and
3-D binary and multicategory images, such as might be found in image
segmentation or classification problems.
You can install the released version of catsim from CRAN with:
install.packages("catsim")
#### or the dev version with:
#devtools::install_github("gzt/catsim")If you have two images, x and y, the simplest method of comparing
them is:
library(catsim)
set.seed(20200505)
x <- besag
y <- x
y[10:20,10:20] <- 1
catsim(x, y, levels = 3)
#> [1] 0By default, this performs 5 levels of downsampling and uses Cohen’s
kappa as the local similarity metric on 11 x 11 windows for a
2-dimensional image and 5 x 5 x 5 windows for a 3-D image. Those can
be adjusted using the levels, method, and window arguments.
Please note that the catsim project is released with a Contributor
Code of Conduct. By
contributing to this project, you agree to abide by its terms.