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 them is:
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.