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thinr  

Binary Image Thinning Algorithms
View on CRAN: Click here


Download and install thinr package within the R console
Install from CRAN:
install.packages("thinr")

Install from Github:
library("remotes")
install_github("cran/thinr")

Install by package version:
library("remotes")
install_version("thinr", "0.2.0")



Attach the package and use:
library("thinr")
Maintained by
Bill Denney
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-05-27
Latest Update: 2026-05-27
Description:
Thinning (skeletonization) algorithms for binary raster images. Provides seven algorithms behind a single dispatching function: Zhang-Suen (Zhang and Suen 1984) <doi:10.1145/357994.358023>, Guo-Hall (Guo and Hall 1989) <doi:10.1145/62065.62074>, a 2-D adaptation of Lee (Lee, Kashyap, and Chu 1994) <doi:10.1006/cgip.1994.1042>, K3M (Saeed, Tabedzki, Rybnik, and Adamski 2010) <doi:10.2478/v10006-010-0024-4>, the parallel form commonly attributed to Hilditch (1969, in 'Machine Intelligence 4'), OPTA / SPTA (Naccache and Shinghal 1984), and Holt and colleagues (1987) <doi:10.1145/12527.12531>. Also provides the medial axis transform (Blum 1967) and a distance transform implementation following Felzenszwalb and Huttenlocher (2012) <doi:10.4086/toc.2012.v008a019>. The drop-in thinImage() matches the signature of thinImage() in the 'EBImage' package on Bioconductor so existing code can switch parsers without changes. The wider thin() API selects the algorithm by name.
How to cite:
Bill Denney (2026). thinr: Binary Image Thinning Algorithms. R package version 0.2.0, https://cran.r-project.org/web/packages/thinr. Accessed 03 Jul. 2026.
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