Fast fuzzy c-means image segmentation

Segment N-dimensional grayscale images into c classes using efficient c-means or fuzzy c-means clustering algorithm


Updated 4 Sep 2020

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Fast N-D Grayscale Image Segmenation With c- or Fuzzy c-Means

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c-means and fuzzy c-means clustering are two very popular image segmentation algorithms. While their implementation is straightforward, if realized naively it will lead to substantial overhead in execution time and memory consumption. Although these deficiencies could be ignored for small 2D images they become more noticeable for large 3D datasets. This submission is intended to provide an efficient implementation of these algorithms for segmenting N-dimensional grayscale images. The computational efficiency is achieved by using the histogram of the image intensities during the clustering process instead of the raw image data. Finally, since the algorithms are implemented from scratch there are no dependencies on any auxiliary toolboxes.

For a quick demonstration of how to use the functions, run the attached DemoFCM.m file.

You can also get a copy of this repo from Matlab Central File Exchange.


MIT © 2019 Anton Semechko

Cite As

Anton Semechko (2023). Fast fuzzy c-means image segmentation (, GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2011a
Compatible with any release
Platform Compatibility
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Version Published Release Notes

Use from GitHub

- title typo

- updated submission description

migrated to GitHub

Included a function that transforms 1D fuzzy memberships to fuzzy membership maps.

To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.