Determination of Probabilistic Distribution Function of Background and Defect Optical Densities for X-Ray Radiography Images of a Steel Plate
Source abstract
Abstract
Usually Gaussian function is used in mathematical modeling of radiographic images output and designing the related image processing filters. The aim of this work is to examine 40 distribution functions which can be used to define the probability distribution function of radiographic films. For this purpose, steel plates with dimensions of 30 \(\times \) 30 cm\(2 \) and thickness of 2 and 8 mm were initially exposed to 80–200 keV X-ray with different radiation intensities as referred in the text. These plates were initially fault-free but some defects were embedded intentionally in selected parts. After radiography, the obtained films were converted to digital images using a laser scanner. Using the digital images, 39 different probability distribution functions were fitted to the obtained histograms. In addition, a new fitting function to predict the histograms treatments is developed by the authors and named Sasanpour–Taheri-proposed Function (STF). The best fitted probability distribution function for the background optical density was STF. Fitting operation was also done for prediction of defects related to density function, where the STF show best results.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advanced X-ray and CT Imaging · Medical Imaging Techniques and Applications · Image and Signal Denoising Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (22)
Show source-provided references
1. Ewert, U., Zscherpel, U.: Replacement of film radiography by digital techniques and enhancement of image quality. Published NDT.net, 06 (2007)
2. Sosnin, F.R.: Estimation of parameters of radiographic images of defects. J. Defectosc. No. 4, 40–44 (1988)
3. AlSaeed, D.H., et al.: Two modified Otsu image segmentation methods based On lognormal and gamma distribution models. International Conference on Information Technology and e-Services (2012)10.1109/ICITeS.2012.6216680 ↗
4. Zaart, A.E., et al.: SAR images segmentation using mixture of gamma distribution. Vision Interface ’99, Trois-Rivières, Canada, 19–21 May, pp. 125–130
5. El-Zaart, A.: Images thresholding using ISODATA techingque with gamma distribution. Pattern Recognit. Image Anal. J. 20(1), 29–41 (2010)10.1134/S1054661810010037 ↗
6. Gao, G., Qin, X., Zhou, S.: Modeling SAR images based on a generalized gamma distribution for texture component. J. Prog. Electromagn. Res. 137, 669–685 (2013)10.2528/PIER13011807 ↗
7. Kumar, V., et al.: Comparative performance analysis of image de-noising techniques. International Conference on Innovations in Engineering and Technology (ICIET’2013), pp. 47–52. Bangkok (Thailand), 25–26 Dec 2013
8. Achim, A., Kuruglu, E.E., Zerubia, J.: SAR image filtering based on the heavy-tailed Rayleigh model. IEEE Trans. Image Process. 15(9), 2686–2693 (2006)10.1109/TIP.2006.877362 ↗
9. Huang, X., Madoc, A.C.: Image and its Noise Removal in Nakagami Fading Channels, ICA0T2006, pp. 570–573, 20–22 Feb 200610.1109/ICACT.2006.206034 ↗
10. EN 444: Non-destructive testing—General principles for radiographic examination of metallic materials by X- and gamma-rays, Feb 1994
11. EN 462-1: Non-destructive testing—Image quality of radiographs—Part 1: image quality indicators (wire type)—Determination of image quality value (1994)
12. EN 473: Non-destructive testing—Qualification and certification of NDT personnel—General principles (2000)
13. EN 1435: Non-destructive testing of welds—Radiographic testing of welded joints. Aug 1997 (includes amendments A1:2002 and A2:2003)
14. EN 12681: Founding—Radiographic examination. Jan 2003
15. EN 584-2: Non-destructive testing—Industrial radiographic film—Part 2: control of film processing by means of reference values (1996)
16. Nacdfeddine, N., Hamami, L., Ziou, D.: Statistical tools for weld defect evaluation in radiographic testing. ECNDT 2006 - Mo.2.4.4, pp. 1–21
17. Alghalandis, S.M., Alamdari, Gh.N.: Welding defect pattern recognition in radiographic images of gas pipelines using adaptive feature extraction method and neural network classifier. 23rd World Gas Conference, pp. 1–13. Amsterdam (2006)
18. Zhang, B., et al.: Research on change detection in remote sensing images by using 2D-Fisher criterion function method. In: Wagner W., Székely, B. (eds.) ISPRS TC VII Symposium—100 Years ISPRS, IAPRS, vol. XXXVIII, Part 7B. Vienna, Austria, 5–7July 2010
19. Liu, X.F., Zhu, X.: Kernel-optimized based Fisher Classification of Hyperspectral Imagery. J. Inf. Hiding Multimed. Signal Process. c 2014 ISSN 2073-4212, Ubiquitous International 5(3), (2014)
20. Kayabol, K., Zerubia, J.: Unsupervised amplitude and texture classification of SAR images with multinomial latent model. 2011 IEEE
21. Krylov, V., et al.: On the method of logarithmic cumulants for parametric probability density function estimation. Inst. Electr. Electron. Eng. (IEEE) 22(10), 3791–3806 (2013)
22. Gonzalez, R.C., Woods, R.E.: Digital image processing, 3rd edn. (2008)