Abstract
Image recognition detects the comprehensive characteristics of rock pore structures. As a result, it has been widely used in the identification of micro-cracks and pores in rocks. Accurate image segmentation is a vital task in the most frequently used image recognition methods (scanning electron microscopy, X-ray tomography, and cast-slice). Among various methods, manual threshold segmentation is still one of the most widely used scanning electron microscopy (SEM) image segmentation methods in rocks. But due to its subjectivity, manual threshold segmentation hardly ensures the uniqueness of the result. In this paper, a new image segmentation method (i.e., the parametric method) is proposed by considering the manual threshold range, skewed Gaussian distribution characteristics of sandy mudstone, and efficient parameters. Based on a typical sandy mudstone SEM image, the reliability of iterative, Otsu’s, and parametric methods was 82.59%, 83.31%, and 91.13%, respectively. Besides, based on the comparative analysis of the gray histogram of pores and matrix, the parametric method yielded a more reasonable outcome.
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Acknowledgements
This research is supported by the National Nature Science Foundation of China Grant, No. U2034203, No. 51679127, and No. 51979151; National Natural Science Foundation of China Youth Fund Project, No. 51809151; and sponsored by Research Fund for Excellent Dissertation of China Three Gorges University, No. 2019BSPY003. Three anonymous reviewers provided helpful and constructive comments that improved the manuscript substantially.
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The software MATLAB (R2016a version) was used to process and calculate the images in this study, and no custom code was used in this study.
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This research is supported by the National Nature Science Foundation of China Grant, No. U2034203, No. 51679127, and No. 51979151; National Natural Science Foundation of China Youth Fund Project, No. 51809151; and sponsored by Research Fund for Excellent Dissertation of China Three Gorges University, No. 2019BSPY003.
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Meiling Zhou and Huafeng Deng conceived the research. Jianlin Li revised it critically for important intellectual content. Weijie Xu and Feng Xu acquired and analyzed the data. Eleyas Assefa checked and revised the language and terminology of the manuscript. Meiling Zhou wrote the manuscript with input from all authors. All authors critically reviewed the manuscript.
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Responsible Editor: Zeynal Abiddin Erguler
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Zhou, M., Li, J., Xu, W. et al. A new parametric segmentation method based on sandy mudstone SEM images. Arab J Geosci 14, 1781 (2021). https://doi.org/10.1007/s12517-021-08189-7
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DOI: https://doi.org/10.1007/s12517-021-08189-7