https://github.com/gzt/catsim

A structural similarity index for binary or categorical images that works in 2D or 3D.
https://github.com/gzt/catsim

binary-data binary-image-classification binary-image-processing categorical-data categorical-images classification image-processing r

Last synced: 2 months ago
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A structural similarity index for binary or categorical images that works in 2D or 3D.

• Host: GitHub
• URL: https://github.com/gzt/catsim
• Owner: gzt
• License: gpl-3.0
• Created: 2018-12-07T21:55:57.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2024-09-30T19:54:30.000Z (3 months ago)
• Last Synced: 2024-10-24T15:55:26.573Z (2 months ago)
• Topics: binary-data, binary-image-classification, binary-image-processing, categorical-data, categorical-images, classification, image-processing, r
• Language: R
• Homepage: https://gzt.github.io/catsim
• Size: 1.78 MB
• Stars: 5
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.Rmd
  • Changelog: NEWS.md
  • Contributing: .github/CONTRIBUTING.md
  • License: LICENSE.md
  • Code of conduct: .github/CODE_OF_CONDUCT.md
  • Support: .github/SUPPORT.md

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README

        
---

output: github_document

---

```{r setup, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```

[![lifecycle](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)

  [![Coverage status](https://codecov.io/gh/gzt/catsim/branch/master/graph/badge.svg)](https://app.codecov.io/github/gzt/catsim?branch=master)

  [![R build status](https://github.com/gzt/catsim/workflows/R-CMD-check/badge.svg)](https://github.com/gzt/catsim/actions)

  

# `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](https://en.wikipedia.org/wiki/Structural_similarity) 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.

## Installation

You can install the released version of catsim from [CRAN](https://CRAN.R-project.org) with:

```r

install.packages("catsim")

#### or the dev version with:

#devtools::install_github("gzt/catsim")

```

## Usage

If you have two images, `x` and `y`, the simplest method of comparing them is:

```{r displayimage}

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](https://gzt.github.io/catsim/CODE_OF_CONDUCT.html).

By contributing to this project, you agree to abide by its terms.