knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
catsim
: a Categorical Image Similarity IndexThe 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)
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 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.
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