An Advanced Image Processing Technique for Backscatter-Electron Data by Scanning Electron Microscopy for Microscale Rock Exploration

Zhaoliang Hou, Kunfeng Qiu, Tong Zhou, Yiwei Cai

Journal of Earth Science ›› 2024, Vol. 35 ›› Issue (1) : 301-305. DOI: 10.1007/s12583-024-1969-9
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An Advanced Image Processing Technique for Backscatter-Electron Data by Scanning Electron Microscopy for Microscale Rock Exploration

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Abstract

Backscatter electron analysis from scanning electron microscopes (BSE-SEM) produces high-resolution image data of both rock samples and thin-sections, showing detailed structural and geochemical (mineralogical) information. This allows an in-depth exploration of the rock microstructures and the coupled chemical characteristics in the BSE-SEM image to be made using image processing techniques. Although image processing is a powerful tool for revealing the more subtle data “hidden” in a picture, it is not a commonly employed method in geoscientific microstructural analysis. Here, we briefly introduce the general principles of image processing, and further discuss its application in studying rock microstructures using BSE-SEM image data.

Keywords

Image processing / rock microstructures / electron-based imaging / data mining

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Zhaoliang Hou, Kunfeng Qiu, Tong Zhou, Yiwei Cai. An Advanced Image Processing Technique for Backscatter-Electron Data by Scanning Electron Microscopy for Microscale Rock Exploration. Journal of Earth Science, 2024, 35(1): 301‒305 https://doi.org/10.1007/s12583-024-1969-9

References

[]
Arganda-Carreras I, Kaynig V, Rueden C, et al.. Trainable Weka Segmentation: A Machine Learning Tool for Microscopy Pixel Classification. Bioinformatics, 2017, 33(15): 2424-2426,
CrossRef Pubmed Google scholar
[]
Boyat, A. K., Joshi, B. K., 2015. A Review Paper: Noise Models in Digital Image Processing. arXiv: 1505.03489. http://arxiv.org/abs/1505.03489
[]
Cheng Q M. IUGS’ Initiative on Data-Driven Geoscience Discovery. Journal of Earth Science, 2021, 32(2): 468-470,
CrossRef Google scholar
[]
Cnudde V, Boone M N. High-Resolution X-Ray Computed Tomography in Geosciences: A Review of the Current Technology and Applications. Earth-Science Reviews, 2013, 123: 1-17,
CrossRef Google scholar
[]
De Boever W, Derluyn H, Van Loo D, et al.. Data-Fusion of High Resolution X-Ray CT, SEM and EDS for 3D and Pseudo-3D Chemical and Structural Characterization of Sandstone. Micron, 2015, 74: 15-21,
CrossRef Pubmed Google scholar
[]
El-Gabry E A, Parwani A V, Pantanowitz L. Whole-Slide Imaging: Widening the Scope of Cytopathology. Diagnostic Histopathology, 2014, 20(12): 456-461,
CrossRef Google scholar
[]
Goldstein J I, Newbury D E, Michael J R, et al.. . ImageJ and Fiji. Scanning Electron Microscopy and X-Ray Microanalysis, 2018 New York Springer 187-193
[]
Gonzalez R C, Woods R E. . Digital Image Processing, 2018 4th Ed New York Pearson Education Limited 1009
[]
Hong L, Wan Y F, Jain A. Fingerprint Image Enhancement: Algorithm and Performance Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(8): 777-789,
CrossRef Google scholar
[]
Hou Z L, Fusseis F, Schöpfer M, et al.. Synkinematic Evolution of Stylolite Porosity. Journal of Structural Geology, 2023, 173: 104916,
CrossRef Google scholar
[]
Hou Z L, Woś D, Tschegg C, et al.. Three-Dimensional Mineral Dendrites Reveal a Nonclassical Crystallization Pathway. Geology, 2023, 51(7): 626-630,
CrossRef Google scholar
[]
Jain, V., Seung, H. S., 2008. Natural Image Denoising with Convolutional Networks. Proceedings of the 21st International Conference on Neural Information Processing Systems, December 8–10, 2008, Vancouver, British Columbia, Canada. 769–776. https://doi.org/10.5555/2981780.2981876
[]
Karras, T., Laine, S., Aittala, M., et al., 2020. Analyzing and Improving the Image Quality of StyleGAN. Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle. IEEE. 8107–8116. https://doi.org/10.1109/CVPR42600.2020.00813
[]
Minaee S, Boykov Y, Porikli F, et al.. Image Segmentation Using Deep Learning: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(7): 3523-3542,
Pubmed
[]
Prêt D, Sammartino S, Beaufort D, et al.. A New Method for Quantitative Petrography Based on Image Processing of Chemical Element Maps: Part I. Mineral Mapping Applied to Compacted Bentonites. American Mineralogist, 2010, 95(10): 1379-1388,
CrossRef Google scholar
[]
Reed S J B. . Electron Microprobe Analysis and Scanning Electron Microscopy in Geology, 2005 Cambridge Cambridge University Press 215,
CrossRef Google scholar
[]
Schindelin J, Arganda-Carreras I, Frise E, et al.. Fiji: An Open-Source Platform for Biological-Image Analysis. Nature Methods, 2012, 9(7): 676-682,
CrossRef Pubmed Google scholar
[]
Sonka M, Hlaváč V, Boyle R. . Image Processing Analysis and Machine Vision, 2013 New York Springer 554
[]
Swamy S, Kulkarni P K. A Basic Overview on Image Denoising Techniques. Int. Res. J. Eng. Technol., 2020, 7(5): 850-857
[]
Tschegg C, Hou Z L, Rice A H N, et al.. Fault Zone Structures and Strain Localization in Clinoptilolite-Tuff (Nižný Hrabovec, Slovak Republic). Journal of Structural Geology, 2020, 138: 104090,
CrossRef Google scholar
[]
Wang Z, Bovik A C. . Modern Image Quality Assessment, 2006 California Morgan & Claypool Publishers 146,
CrossRef Google scholar
[]
Xu J, Yang L, Wu D P. Ripplet: A New Transform for Image Processing. Journal of Visual Communication and Image Representation, 2010, 21(7): 627-639,
CrossRef Google scholar
[]
Zehner B, Börner J H, Görz I, et al.. Workflows for Generating Tetrahedral Meshes for Finite Element Simulations on Complex Geological Structures. Computers & Geosciences, 2015, 79: 105-117,
CrossRef Google scholar
[]
Zhang L, Qiu K F, Hou Z L, et al.. Fluid-Rock Reactions of the Triassic Taiyangshan Porphyry Cu-Mo Deposit (West Qinling, China) Constrained by QEMSCAN and Iron Isotope. Ore Geology Reviews, 2021, 132: 104068,
CrossRef Google scholar

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