Journal of Nuclear Science, Engineering and Technology (JONSAT)
In cooperation with the Iranian Nuclear Society

Improvement and comparison of X and Neutron radiography images by Gaussian Convolution Method

Document Type : Research Paper

Authors

A. Movafeghi 1
E. Yahaghi 2
S.H. Mosavian 2
B. Rokrok 1

1 Reactor and Nuclear Safety Research School, Nuclear Science and Technology Research Institute, AEOI, P.O.Box: 14155-1339, Tehran - Iran

2 Physics Department, Basic Science Faculty, Imam Khomeini International University, P.O.Box: 34148-96818, Qazvin – Iran

https://doi.org/10.24200/nst.2022.1353
Abstract
X-rays and neutron radiography images are one of the most effective defects and structure detection methods. The interactions between neutrons and X-rays are different in the material, and therefore, different information can be obtained from the radiographs. Due to neutron and X-rays photon scattering, focal spot size, electronic noises, etc., the images are blurred and their quality is reduced. In this study, while investigating the radiographs of X-rays, and neutrons, the defects and internal structure of objects are investigated. The results show that neutron radiography performs very well in detecting the internal structure of low atomic number materials. X-ray radiography is effective for high atomic numbers as metal. Gaussian convolution is used to enhance the radiography images and reduce blurriness components. The results show that by reducing the background, the blurriness components can be reduced and the defects areas and internal structure of the objects can be better investigated. Specialists evaluated the results in radiography; the results show that the expert’s evaluation approved the image enhancement.

Highlights

1. R. Behling, Modern Diagnostic X-Ray Sources, Technology, Manufacturing, Reliability. Boca Raton, FL, USA: Taylor and Francis, CRC Press. ISBN 9781482241327 (2015).

 

2. E. Negahdarzadeh, et al, Diagnosis of design and defects in radiography of ceramic antiqueobjects using the wavelet- domain hidden Markov models, Journal of Cultural Heritage, 35, 56-63 (2019).

 

3. T. Franzel, U. Schmidt, S. Roth, Object Detection in Multi-view X-Ray Images, In DAGM (2012).

 

4. P. Shaygan-Fard, et al, Enhancement radiography image by two algorithms based on cartoon – texture decomposition, Physica Scripta, No. 065002, 94 (6), 1-7 (2019).

 

5. M. Mirzapour, E. Yahaghi, A. Movafeghi, Comparison of four iterative methods for improving the contrast of the radiography images, Physica Scripta, No. 035001, 94 (3), 1-9 (2018).

 

6. D. Mery, V. Riffo, Automated Object Recognition in Baggage Screening Using Multiple X-ray Views, In 52nd Annual Conference of the British Institute for Non-Destructive Testing, Telford, (2013).

 

7. M. Mansoor, R. Rajashankari, Detection of concealed weapons in Xray images using fuzzy K-NN, International Journal of Computer Science, Engineering and Information Technology, 2(2), (2012).

 

8. I.S. Anderson, Neutron Imaging and Applications, by, Springer, (2009).

 

9. ASTM International, ASTM E748, Practices for Thermal Neutron Radiography of Materials, (2014).

 

10. ASTM International, ASTM E545: Standard Test Method for Determining Image Quality in Direct Thermal Neutron Radiographic Examination, (2014).

 

11. J.S. Brenizer, A review of significant advances in neutron imaging from conception to the present, Physics  Procedia,  43, 10-20 (2013).

 

12. J.J. Henriksen, 3D surface tracking and approximation using Gabor filters, South Denmark University, March 28 (2007).

 

13. R.J. Ferrari, et al, Analysis of Asymmetry in Mammograms via Directional Filtering With Gabor Wavelets, IEEE Trans. on Medical Imaging, 20(9), 953-964, September (2001).

 

14. A. Serrano, et al, Analysis of variance of Gabor filter banks parameters for optimal face recognition, Pattern Recognition Letters, 1998-2008 (2011).

 

15. R.J. Ferrari, et al, Analysis of Asymmetry in Mammograms via Directional Filtering With Gabor Wavelets, IEEE Trans. on Medical  Imaging, 20 (9), 953-964 (2001).

 

16. J. Oh, S. Choi, Selective generation of Gabor features for fast face recognition on mobile devices, Pattern Recognition Letters, 34, (2013).

 

17. P. Kruizinga, N. Petkov, S.E. Grigorescu, Comparison of texture features based on Gabor filters, Proceedings of the 10th International Conference on Image Analysis and processing, (1999).

 

18. P. Kovesi, Phase Preserving Denoising of Images, The Australian Pattern Recognition Society Conference: DICTA'99. Perth WA. pp 212-217 (December1999). http://www.cs.uwa.edu.au /pub/ robvis/ papers/p /denoise.ps.gz.

 

19. https://nray.ca/nray/images.

 

20. ISO 17636-2, Non-destructive testing of welds-Radiographic testing-Part 2: X- and gamma-ray techniques with digital detectors, (2013).

 

21. E.H. Lehmann, S. Hartmann, M.O. Speidel, Investigation of the content of Ancient Tibetan Metallic Buddha Statues by means of Neutron Imaging Methods, Archaeometry, 52 (3), 416-428 (2010).

Keywords

X-rays radiography
Neutron radiography
Structure defects
Image processing
Gaussian convolution
1. R. Behling, Modern Diagnostic X-Ray Sources, Technology, Manufacturing, Reliability. Boca Raton, FL, USA: Taylor and Francis, CRC Press. ISBN 9781482241327 (2015).
 
2. E. Negahdarzadeh, et al, Diagnosis of design and defects in radiography of ceramic antiqueobjects using the wavelet- domain hidden Markov models, Journal of Cultural Heritage, 35, 56-63 (2019).
 
3. T. Franzel, U. Schmidt, S. Roth, Object Detection in Multi-view X-Ray Images, In DAGM (2012).
 
4. P. Shaygan-Fard, et al, Enhancement radiography image by two algorithms based on cartoon – texture decomposition, Physica Scripta, No. 065002, 94 (6), 1-7 (2019).
 
5. M. Mirzapour, E. Yahaghi, A. Movafeghi, Comparison of four iterative methods for improving the contrast of the radiography images, Physica Scripta, No. 035001, 94 (3), 1-9 (2018).
 
6. D. Mery, V. Riffo, Automated Object Recognition in Baggage Screening Using Multiple X-ray Views, In 52nd Annual Conference of the British Institute for Non-Destructive Testing, Telford, (2013).
 
7. M. Mansoor, R. Rajashankari, Detection of concealed weapons in Xray images using fuzzy K-NN, International Journal of Computer Science, Engineering and Information Technology, 2(2), (2012).
 
8. I.S. Anderson, Neutron Imaging and Applications, by, Springer, (2009).
 
9. ASTM International, ASTM E748, Practices for Thermal Neutron Radiography of Materials, (2014).
 
10. ASTM International, ASTM E545: Standard Test Method for Determining Image Quality in Direct Thermal Neutron Radiographic Examination, (2014).
 
11. J.S. Brenizer, A review of significant advances in neutron imaging from conception to the present, Physics  Procedia,  43, 10-20 (2013).
 
12. J.J. Henriksen, 3D surface tracking and approximation using Gabor filters, South Denmark University, March 28 (2007).
 
13. R.J. Ferrari, et al, Analysis of Asymmetry in Mammograms via Directional Filtering With Gabor Wavelets, IEEE Trans. on Medical Imaging, 20(9), 953-964, September (2001).
 
14. A. Serrano, et al, Analysis of variance of Gabor filter banks parameters for optimal face recognition, Pattern Recognition Letters, 1998-2008 (2011).
 
15. R.J. Ferrari, et al, Analysis of Asymmetry in Mammograms via Directional Filtering With Gabor Wavelets, IEEE Trans. on Medical  Imaging, 20 (9), 953-964 (2001).
 
16. J. Oh, S. Choi, Selective generation of Gabor features for fast face recognition on mobile devices, Pattern Recognition Letters, 34, (2013).
 
17. P. Kruizinga, N. Petkov, S.E. Grigorescu, Comparison of texture features based on Gabor filters, Proceedings of the 10th International Conference on Image Analysis and processing, (1999).
 
18. P. Kovesi, Phase Preserving Denoising of Images, The Australian Pattern Recognition Society Conference: DICTA'99. Perth WA. pp 212-217 (December1999). http://www.cs.uwa.edu.au /pub/ robvis/ papers/p /denoise.ps.gz.
 
19. https://nray.ca/nray/images.
 
20. ISO 17636-2, Non-destructive testing of welds-Radiographic testing-Part 2: X- and gamma-ray techniques with digital detectors, (2013).
 
21. E.H. Lehmann, S. Hartmann, M.O. Speidel, Investigation of the content of Ancient Tibetan Metallic Buddha Statues by means of Neutron Imaging Methods, Archaeometry, 52 (3), 416-428 (2010).

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