Package {thinr}

Title:	Binary Image Thinning Algorithms
Version:	0.2.0
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.
License:	LGPL-3
Encoding:	UTF-8
Depends:	R (≥ 4.2)
LinkingTo:	Rcpp
Imports:	Rcpp
Suggests:	bench, covr, knitr, lintr, rmarkdown, styler, testthat (≥ 3.0.0)
VignetteBuilder:	knitr
Config/testthat/edition:	3
RoxygenNote:	8.0.0
URL:	https://github.com/humanpred/thinr, https://humanpred.github.io/thinr/
BugReports:	https://github.com/humanpred/thinr/issues
NeedsCompilation:	yes
Packaged:	2026-05-20 18:04:21 UTC; bill
Author:	Bill Denney [aut, cre] (affiliation: Human Predictions, LLC)
Maintainer:	Bill Denney <wdenney@humanpredictions.com>
Repository:	CRAN
Date/Publication:	2026-05-27 20:10:02 UTC

thinr: Binary Image Thinning Algorithms

Description

Thinning (also called skeletonization) reduces a binary image of a shape to a one-pixel-wide centerline that preserves the shape's topology. thinr provides seven thinning algorithms behind a single dispatching function, plus the medial axis transform and a fast distance transform.

Thinning algorithms (thin(method = ...))

• zhang_suen — Zhang & Suen (1984) doi:10.1145/357994.358023. Default; matches EBImage::thinImage.

• guo_hall — Guo & Hall (1989) doi:10.1145/62065.62074. Often better corner preservation on diagonal features.

• lee — Lee, Kashyap & Chu (1994) doi:10.1006/cgip.1994.1042, 2-D adaptation. Four directional sub-iterations.

• k3m — Saeed, Tabędzki, Rybnik & Adamski (2010) doi:10.2478/v10006-010-0024-4. Six-phase lookup-table thinning.

• hilditch — parallel form commonly attributed to Hilditch (1969, in Machine Intelligence 4). Single-pass thinning with look-ahead crossing-number check.

• opta — Naccache & Shinghal (1984), "An investigation into the skeletonization approach of Hilditch", Pattern Recognition 17(3):279-284. One-pass safe-point thinning (SPTA).

• holt — Holt, Stewart, Clint & Perrott (1987) doi:10.1145/12527.12531. One-subcycle parallel thinning with edge information about neighbours.

See thin() and vignette("choosing-a-method") for guidance.

Medial axis and distance transform

• medial_axis() — Medial axis transform (Blum 1967): the locus of foreground pixels that are ridge points of the distance transform. Optionally returns the per-pixel distance.

• distance_transform() — Euclidean (Felzenszwalb-Huttenlocher 2012, linear-time separable), Manhattan, or Chessboard distance from each foreground pixel to the nearest background pixel.

Drop-in compatibility

thinImage() matches the signature of EBImage::thinImage(). Code that uses EBImage::thinImage can switch to thinr::thinImage with no other changes.

Author(s)

Maintainer: Bill Denney wdenney@humanpredictions.com (ORCID) (affiliation: Human Predictions, LLC)

Authors:

• Bill Denney wdenney@humanpredictions.com (ORCID) (affiliation: Human Predictions, LLC)

See Also

Useful links:

• https://github.com/humanpred/thinr

• https://humanpred.github.io/thinr/

• Report bugs at https://github.com/humanpred/thinr/issues

Distance transform of a binary image

Description

Compute the distance from each foreground pixel to the nearest background pixel, under one of three standard metrics.

Usage

distance_transform(image, metric = c("euclidean", "manhattan", "chessboard"))

Arguments

Value

A numeric matrix of the same shape as image. Background pixels are 0; foreground pixels carry their distance to the nearest background pixel.

References

Felzenszwalb, P. F., & Huttenlocher, D. P. (2012). Distance transforms of sampled functions. Theory of Computing, 8(19), 415-428. doi:10.4086/toc.2012.v008a019

Rosenfeld, A., & Pfaltz, J. L. (1968). Distance functions on digital pictures. Pattern Recognition, 1(1), 33-61. doi:10.1016/0031-3203(68)90013-7

Examples

# A 5x5 image with a single background pixel in the corner.
m <- matrix(1L, nrow = 5, ncol = 5)
m[1, 1] <- 0L
distance_transform(m, metric = "manhattan")
distance_transform(m, metric = "chessboard")
round(distance_transform(m, metric = "euclidean"), 3)

Medial axis transform

Description

Return the medial axis of a binary image: the locus of foreground pixels that are local maxima of the (squared) Euclidean distance transform in at least one of the four principal directions (horizontal, vertical, NW-SE diagonal, NE-SW diagonal). Each skeleton pixel carries width information via the distance value at that point.

Usage

medial_axis(image, return_distance = FALSE)

Arguments

Details

This is different from thin(): classical thinning algorithms produce a connected, 1-pixel-wide skeleton without width information. The medial axis transform (Blum 1967) produces a skeleton with width information, useful for shape analysis where local thickness matters.

Value

Either a matrix (when return_distance = FALSE) or a list(skeleton, distance) (when return_distance = TRUE).

References

Blum, H. (1967). A transformation for extracting new descriptors of shape. In Models for the Perception of Speech and Visual Form (pp. 362-380). MIT Press.

Felzenszwalb, P. F., & Huttenlocher, D. P. (2012). Distance transforms of sampled functions. Theory of Computing, 8(19), 415-428. doi:10.4086/toc.2012.v008a019

Examples

# A 7x9 solid rectangle: the medial axis is the middle row.
m <- matrix(0L, nrow = 7, ncol = 9)
m[3:5, 3:7] <- 1L
medial_axis(m)

# Returning width information alongside the skeleton.
result <- medial_axis(m, return_distance = TRUE)
result$skeleton
round(result$distance, 3)

Thin (skeletonize) a binary image

Description

Reduce a binary image to its one-pixel-wide skeleton using one of the supported thinning algorithms.

Usage

thin(
  image,
  method = c("zhang_suen", "guo_hall", "lee", "k3m", "hilditch", "opta", "holt"),
  max_iter = 1000L
)

Arguments

Value

A matrix of the same shape and storage mode as image, with foreground pixels marking the thinned skeleton and the rest set to background.

Examples

# A 3x3 solid square thins to a single foreground pixel.
m <- matrix(c(0, 0, 0, 0, 0,
              0, 1, 1, 1, 0,
              0, 1, 1, 1, 0,
              0, 1, 1, 1, 0,
              0, 0, 0, 0, 0),
            nrow = 5, byrow = TRUE)
thin(m, method = "zhang_suen")
thin(m, method = "guo_hall")
thin(m, method = "hilditch")

Drop-in replacement for EBImage::thinImage

Description

Applies Zhang-Suen thinning to a binary image. Provided as a signature-compatible alternative to EBImage::thinImage() so callers can switch from EBImage to thinr by changing the namespace prefix only.

Usage

thinImage(x)

Arguments

Details

For access to the other algorithms (Guo-Hall, and eventually Lee / K3M), use thin().

Value

The thinned skeleton in the same storage mode as x.

Examples

m <- matrix(c(0, 1, 1, 1, 0,
              0, 1, 1, 1, 0,
              0, 1, 1, 1, 0),
            nrow = 3, byrow = TRUE)
thinImage(m)