catsim: a Categorical Image Similarity Index
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,
y, the simplest method of comparing
library(catsim) set.seed(20200505) x <- besag y <- x y[10:20,10:20] <- 1 catsim(x, y, levels = 3) #>  0.8897574
By 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
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.
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.