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Extending morphological pattern segmentation to 3D voxels



This short communication introduces the logic, demonstrates its use, and identifies the availability of a new tool that extends the traditional 2D morphological segmentation of binary raster data into the 3-dimensional realm of voxels.


A combination of 3-dimensional array data and network graph theory are implemented to facilitate the logical parsing of identified 3-dimensional features into their mutually exclusive constituent morphological classes.


A combination of 3-dimensional array data and network graph theory are implemented to facilitate the logical parsing of identified 3-dimensional features into their mutually exclusive constituent morphological classes. All processing is performed in the R environment, providing the ability for anyone to perform the demonstrated analyses on their own data. The only input requirement is a binary (1 = feature of interest, 0 otherwise) 3-dimensional array, where each voxel of interest is then classified into classes called outside, mass, skin, crumb, antenna, circuit, bond, and void that correspond their 2-dimensional equivalents of background, core, edge, islet, branch, loop, bridge, and perforation. An additional class called the void-volume identifies voxels belonging to the empty space within the object of interest.


The work helps to bring pattern metrics into the 3-dimensional world, particularly given the reliance on adjacency and connectivity assessments.

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Data availability

Not applicable.

Code availability

The code for achieving 3D morphological segmentation is being prepared for free and open distribution on the Comprehensive R Archive Network. The code is also available from the author of this short communication.


  1. morph3d is a complete software tool and will be available via CRAN (Comprehensive R Archive Network) for free and open distribution in the package morph. A copy of the code can also be obtained from the author.


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I acknowledge the valuable and constructive feedback from two anonymous reviewers whose advice and suggestions helped improve the clarity of this manuscript. I value discussions with Dr. Connie Ko who was an excellent sounding board for ideas. This work was funded through a Discovery Grant from the Natural Sciences and Engineering Research Council of Canada.


This work was funded by a Natural Sciences and Engineering Research Council (NSERC) Discovery Grant (RGPIN-2021-03645) to the author.

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I conceived the idea of 3D morphological segmentation, developed the logic, wrote the code, and developed this short communication without external assistance.

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Correspondence to Tarmo K. Remmel.

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Remmel, T.K. Extending morphological pattern segmentation to 3D voxels. Landsc Ecol 37, 373–380 (2022).

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  • Morphology
  • Landscape pattern
  • 3D segmentation
  • Volumetric data
  • Landscape structure