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

Determination of Probability Distribution Function of Optical Density in Industrial Radiographic Films by X-Ray

Document Type : Research Paper

Authors

M. T Sasanpour
A Taheri
R. G Peyvandi
Abstract
In this paper, the optical density distribution function obtained from the radiographic films at the voltages of 80-150 kV was investigated. Steel plates of 30×30 mm2 with thicknesses of 2 and 8 mm were irradiated according to the European standards with different currents at various times. The information of the radiographic films was converted into 8-bit numeric data by using a laser scanner with a resolution of 3200 dpi. The histograms obtained from these scans were compared with all applicable probability functions. Due to the large number of the employed probability functions, their compatibility was first assessed on the best and simplest histogram. Afterwards, the selected functions were used for the remaining films. In this way, the best probability distribution function was determined. The same steps were taken for radiographic defects. Furthermore, the degree of adaptation of the probability distribution functions applied to the base metal and the defects regions was also evaluated.

Highlights

[1] U. Ewert, U. Zscherpel, Replacement of film radiography by digital techniques and enhancement of image quality, NDT.net 12(6) (2007) 14.

 

[2] F.R. Sasnin, Estimation of parameters of radiographic images of defects, RUSS. J. NONDESTRUCT 4 (1988) 40-44.

 

[3] A. El-Zaart, Synthetic aperture radar images segmentation using minimum cross entropy with Gamma distribution, SIPIJ 6 (2015) 19-31.

 

[4] G. Gao, X. Qin, S. Zhou, Modeling SAR images based on a generalized gamma distribution for texture component, PIER Journal 137 (2013) 669-685.

 

[5] A.M. Achim, E.E. Kuruoglu, J. Zerubia, Maximum a posteriori estimation of radar cross section in SAR images using the heavy-tailed Rayleigh model, EUSIPCO 2005,IEEE Conference Publications(2005) 1-4.

 

[6] A. Achim, E.E. Kuruglu, J. Zerubia, SAR Image Filtering Based on the Heavy-Tailed Rayleigh Model, IEEE Trans. Image Proc. 15 (9) (2006) 2686-2693.

 

[7] S. Sayama, S. Ishii, Suppression of Log-Normal Distributed Weather Clutter Observed by an S-Band Radar, WET 4(3) (2013) 125-133.

 

[8] J. Liang, Q. Liang, S. Samn, A Propagation Environment Modeling in Foliage, EURASIP. J. Wirel. Commun. Netw. (2010) 12.

 

[9] G. Moser, J. Zerubia, S.B. Serpico, SAR Amplitude Probability Density Function Estimation Based on a Generalized Gaussian Model, IEEE Trans. Image Proc. 15 (6) (2006) 1428-1442.

 

[10] X. Huang, A.C. Madoc, Image and Its Noise Removal in Nakagami Fading Channels, ICACT 2006 1 (2006) 570-573.

 

[11] EN 444, Non-destructive testing—General principles for radiographic examination of metallic materials by X- and gamma-rays (1994).

 

[12] EN 462-1, Non-destructive testing–Image quality of radiographs–Part 1: Image quality indicators (wire type)–Determination of image quality value (1994).

[13] EN 473, Non-destructive testing-Qualification and certification of NDT personnel-General principles (2000).

 

[14] EN 584-2, Non-destructive testing–Industrial radiographic film–Part 2: Control of film processing by means of reference value.

 

[15] EN 1435, Non-destructive testing of welds—Radiographic testing of welded joints (includes amendments A1:2002 and A2:2003) (1997).

 

[16] EN 12681, Founding-Radiographic examination (2003).

 

[17] H.E. Johns, J.R. Canningham, The physics of radiology, fourth edition (1983) 796.

 

[18] J.H. Hubbell, Photon cross sections, attenuation coefficients, and energy absorption coefficients from 10 keV to 100 GeV, NSRDS-NBS 29 (1969) 85.

 

[19] G. Abdel-Azim, Z.A. Abo-Eleneen, A Novel Algorithm for Image Thresholding Using non Parametric Fisher Information, ECEA-1 1 (2014) 12.

 

[20] H. Wang, P. Li, T. Zhang, Histogram feature-based Fisher linear discriminant for face detection, Neural Comput. Appl. 17 (2008) 49–58.

 

[21] I. Valavanis, D. Kosmopoulos, Multiclass defect detection and classification in weld radiographic images using geometric and texture features, Expert Syst. Appl. 37 (2010) 7606-7614.

 

[22] R. Hou, D. Du, J. Shao, L. Wang, B. Chang, Segmentation of Weld Defects in X-ray Image Based on Partial Surface Reconstruction, 17th WCNDT, Shanghai, China 92 (2008)12.

 

[23] B. Venkatraman, M.M. Anishin Raj, V. Vaithiyanathan, Weld Defect Detection Using Iterative Image Reconstruction Methods, Indian. J. Sci. Technol. 6 (4) (2013) 4378-4383.

 

[24] M. Tridi, S. Belaifa, N. Nacereddine, Weld defect classification using EM algorithm for Gaussian mixture model, SETIT 2005, TUNISIA (2005) 6.

Keywords

Radiographic Film
X-Ray
Probability Distribution Function
Histogram
Radiography
[1] U. Ewert, U. Zscherpel, Replacement of film radiography by digital techniques and enhancement of image quality, NDT.net 12(6) (2007) 14.
 
[2] F.R. Sasnin, Estimation of parameters of radiographic images of defects, RUSS. J. NONDESTRUCT 4 (1988) 40-44.
 
[3] A. El-Zaart, Synthetic aperture radar images segmentation using minimum cross entropy with Gamma distribution, SIPIJ 6 (2015) 19-31.
 
[4] G. Gao, X. Qin, S. Zhou, Modeling SAR images based on a generalized gamma distribution for texture component, PIER Journal 137 (2013) 669-685.
 
[5] A.M. Achim, E.E. Kuruoglu, J. Zerubia, Maximum a posteriori estimation of radar cross section in SAR images using the heavy-tailed Rayleigh model, EUSIPCO 2005,IEEE Conference Publications(2005) 1-4.
 
[6] A. Achim, E.E. Kuruglu, J. Zerubia, SAR Image Filtering Based on the Heavy-Tailed Rayleigh Model, IEEE Trans. Image Proc. 15 (9) (2006) 2686-2693.
 
[7] S. Sayama, S. Ishii, Suppression of Log-Normal Distributed Weather Clutter Observed by an S-Band Radar, WET 4(3) (2013) 125-133.
 
[8] J. Liang, Q. Liang, S. Samn, A Propagation Environment Modeling in Foliage, EURASIP. J. Wirel. Commun. Netw. (2010) 12.
 
[9] G. Moser, J. Zerubia, S.B. Serpico, SAR Amplitude Probability Density Function Estimation Based on a Generalized Gaussian Model, IEEE Trans. Image Proc. 15 (6) (2006) 1428-1442.
 
[10] X. Huang, A.C. Madoc, Image and Its Noise Removal in Nakagami Fading Channels, ICACT 2006 1 (2006) 570-573.
 
[11] EN 444, Non-destructive testing—General principles for radiographic examination of metallic materials by X- and gamma-rays (1994).
 
[12] EN 462-1, Non-destructive testing–Image quality of radiographs–Part 1: Image quality indicators (wire type)–Determination of image quality value (1994).
[13] EN 473, Non-destructive testing-Qualification and certification of NDT personnel-General principles (2000).
 
[14] EN 584-2, Non-destructive testing–Industrial radiographic film–Part 2: Control of film processing by means of reference value.
 
[15] EN 1435, Non-destructive testing of welds—Radiographic testing of welded joints (includes amendments A1:2002 and A2:2003) (1997).
 
[16] EN 12681, Founding-Radiographic examination (2003).
 
[17] H.E. Johns, J.R. Canningham, The physics of radiology, fourth edition (1983) 796.
 
[18] J.H. Hubbell, Photon cross sections, attenuation coefficients, and energy absorption coefficients from 10 keV to 100 GeV, NSRDS-NBS 29 (1969) 85.
 
[19] G. Abdel-Azim, Z.A. Abo-Eleneen, A Novel Algorithm for Image Thresholding Using non Parametric Fisher Information, ECEA-1 1 (2014) 12.
 
[20] H. Wang, P. Li, T. Zhang, Histogram feature-based Fisher linear discriminant for face detection, Neural Comput. Appl. 17 (2008) 49–58.
 
[21] I. Valavanis, D. Kosmopoulos, Multiclass defect detection and classification in weld radiographic images using geometric and texture features, Expert Syst. Appl. 37 (2010) 7606-7614.
 
[22] R. Hou, D. Du, J. Shao, L. Wang, B. Chang, Segmentation of Weld Defects in X-ray Image Based on Partial Surface Reconstruction, 17th WCNDT, Shanghai, China 92 (2008)12.
 
[23] B. Venkatraman, M.M. Anishin Raj, V. Vaithiyanathan, Weld Defect Detection Using Iterative Image Reconstruction Methods, Indian. J. Sci. Technol. 6 (4) (2013) 4378-4383.
 
[24] M. Tridi, S. Belaifa, N. Nacereddine, Weld defect classification using EM algorithm for Gaussian mixture model, SETIT 2005, TUNISIA (2005) 6.

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