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. 2019 Apr 30;9:6684. doi: 10.1038/s41598-019-43130-9

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© The Author(s) 2019

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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Schematic illustration of the 3D image post-treatment work-flow. Each sub-figure represents the result of one step of the algorithm. The links between them describe the operations needed to pursue one step after another. (a) Downscaled original image before processing, (b1) image filtered using a customized Kuwahara filter (each pixel is replaced with the mean of the means/variances ratio evaluated at neighbouring boxes), (b2) downscaled original image binarized using a simple threshold, (c) filtered image binarized using a simple threshold and cleaned using mathematical morphology closure treatments to fill gaps and remove smallest “islands”, (d1) result of distance map, (d2) gradient image using Sobel method, (e) binarized image using simple threshold, (f) seeds for watershed obtained by computing connected components, (g) watershed image obtained using flooding method, (h) pre-segmented image obtained from original downscaled binarized image and watershed image by point-wise multiplication (result from (b2) and (g)), (i) final segmented image after a graph merging method based on evaluation of contact surface between relative to mean total surface for every pair of pre-segmented subunits. Each colour codes one 3D segmented unit. One can note that the segmentation procedure finally detects four different units (white asterisks, picture “i”) whereas human eye would distinguish seven units (yellow points, picture “a”).