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. 2023 Jun 7;10:368. doi: 10.1038/s41597-023-02259-z

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 6 — Effect of the segmentation algorithm on computed porosity. (a) Filtered grayscale image from sample 5A. The colorbar represents the grayscale level from 0 to 255. (b) Processed image segmented by means of the Multi-Otsu algorithm (n = 3) shows three distinct phases: the solid matrix in yellow, void space in black, and the intermediary class in green. (c) Segmented images using the Otsu algorithm and (d) Multi-Otsu method after merging the solid matrix and intermediary classes. Yellow represents the calculated solid matrix from merging the two classes and purple represents the void space. The side length of each image is 5.625 mm.