Abstract
2D CT radiographic images are widely used in industrial as well as medical applications to examine different types of objects whenever non-destructive measurements of quality are necessary. To extract meaningful structural information for the scanned object from a low-dose input without increasing the radiation level of the scanner, we propose and experimentally investigate a novel two-step process. Firstly, the image is denoised by a regularization method in order to remove unwanted disturbances which affect its quality. Secondly, the difference images between the outputs of different regularization methods are used for feature localization and extraction. The theory as well as the numerical results of the application of several methods on real-life industrial CT data are presented and compared herein.
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Notes
- 1.
Available at http://www.cs.tut.fi/~foi/invansc/.
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Acknowledgements
This work is a continuation of the research, performed during the ECMI Modelling Week, July 17–24, 2016, Sofia, Bulgaria. We are grateful to Ivan Georgiev (IICT-BAS) for providing us with real-life industrial CT data. The work of S. Harizanov has been partially supported by the “Program for career development of young scientists, BAS”, grant No. DFNP-92/04.05.2016 and by the Bulgarian National Science Fund under grant No. BNSF-DM02/2 from 17.12.2016. The work of D. Wenzel has been supported by the Institute of Numerical Analysis of Dresden University of Technology.
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Harizanov, S., de Dios Pont, J., Ståhl, S., Wenzel, D. (2018). Noise Removal and Feature Extraction of 2D CT Radiographic Images. In: Georgiev, K., Todorov, M., Georgiev, I. (eds) Advanced Computing in Industrial Mathematics. Studies in Computational Intelligence, vol 728. Springer, Cham. https://doi.org/10.1007/978-3-319-65530-7_6
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DOI: https://doi.org/10.1007/978-3-319-65530-7_6
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