Abstract
Three sets of synthetic images were created from two original datasets. A suite exhibiting greyscale contrast was produced from an 8.96-μm voxel size 3D X-ray microscopy image of a sandstone rock and a two suites (one showing greyscale contrast and one showing both greyscale and textural contrast) were produced from a 5 × 5 × 5 nm voxel size FIB-SEM image of a shale rock. The performance of three image segmentation algorithms (global multi-Otsu thresholding, seeded watershed region growing, and machine learning-based multivariant classification) was then assessed by their ability to recover their respective original segmented 3D images. While all algorithms performed well at low noise levels, machine learning-based classification proved significantly more noise tolerant than either of the traditional algorithms. It was also able to segment the non-greyscale (textural based) contrast, something the traditional completely failed to do, with voxel misclassification rates for the traditional techniques above 50% at a 0 noise level within the textural contrast regions. Machine learning-based classification, in contrast, achieved misclassification rates of less than 5% in the same regions.
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21 September 2018
The original version of this article unfortunately contained mistakes introduced during the production process. The corrections are given in the following list: (1) Figure 6 was missing.
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Acknowledgements
I thank ZEISS microscopy, especially the ZEISS Zen Intellesis team, for the equipment and software access required for this work. I also thank Dr. Lori Hathon, Dr. Sreenivas Bhattiprolu and Dr. Lorenz Lechner for help and discussion.
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All work was performed by the sole author of this manuscript (Dr. Matthew Andrew).
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The original version of this article was revised: Figure 6 and it’s corresponding citation in the text were missing. Please refer to the Correction article for the complete list of changes.
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Andrew, M. A quantified study of segmentation techniques on synthetic geological XRM and FIB-SEM images. Comput Geosci 22, 1503–1512 (2018). https://doi.org/10.1007/s10596-018-9768-y
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DOI: https://doi.org/10.1007/s10596-018-9768-y