Main Article Content
Abstract
Digital radiography incresingly is being applied in the fabrication industry. Compared to film- based radiography, digitally radiographed images can be acquired with less time and fewer exposures. However, noises can simply occur on the digital image resulting in a low-quality result. Due to this and the system’s complexity, parameters’ sensitivity, and environmental effects, the results can be difficult to interpret, even for a radiographer. Therefore, the need of an application tool to improve and evaluate the image is becoming urgent. In this research, a user-friendly tool for image processing and image quality measurement was developed. The resulting tool contains important components needed by radiograph inspectors in analyzing defects and recording the results. This tool was written by using image processing and the graphical user interface development environment and compiler (GUIDE) toolbox available in Matrix Laboratory (MATLAB) R2008a. In image processing methods, contrast adjustment, and noise removal, edge detection was applied. In image quality measurement methods, mean square error (MSE), peak signal-to-noise ratio (PSNR), modulation transfer function (MTF), normalized signal-to-noise ratio (SNRnorm), sensitivity and unsharpness were used to measure the image quality. The graphical user interface (GUI) wass then compiled to build a Windows, stand-alone application that enables this tool to be executed independently without the installation of MATLAB.
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References
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References
Edwin EP, Williamson GF (2002), Digital radiography: An Overview. The Journal of Contemporary Dental Practice 3(4):23-39.
Ewert U, Zscherpel U, Klaus B (2007), Strategies for film replacement in radiography- a comparative study. IV Conferencia Panamericana de END, Buenos Aires.
Han X, Deng F, Yin X (2009), An approach of adaptive enhancement of X-ray welding image. International Conference on Information Engineering and Computer Science (ICIECS) 1- 4.
Hendee WR, Russell RE (2002), Medical imaging physics. Fourth edition, ISBN: 0-471-38226-4, Copyright C_ 2002 Wiley-Liss, Inc.
Nadila PZ, Manurung YHP (2010), Advanced ndt using μ-focussed digital radiography for welding inspection. 1st Conference of National Postgraduate Seminar NAPAS, UiTM.
Noorhazleena (2010) Computed radiography (CR) signal to noise ratio (SNR) study based on thickness changes of steel step wedge. Journal of Malaysian Society for Non-Destructive Testing MSNT.
Pardikar RJ (2008), Digital radiography and Computed radiography for Enhancing the Quality and Productivity of Weldments in Boiler components. 17th World Conference on Nondestructive Testing, Shanghai, China.
Rafael CG, Richard EW (1992), Digital image processing. Addison- Wesley Publishing Company.
Samei E, Flynn MJ (2003), An experimental comparison of detector performance for direct and indi-rect digital adiography systems. Medical Physics 30(4):608-622.
Samei E, Fynn MJ, Reimann DA (1998), Measuring the pre sampled MTF of Digital Radiographic Systems. J. of Medical Physics 25(1):102-113.
Uwe E, Uwe Z, Klaus B (2007), Possibilities and limits of digital industrial radiology - The new high contrast sensitivity technique - Examples and system theoretical analysis. Int. Symposium on Digital industrial Radiology and Computed Tomography, Lyon, France.
Yeong-Taekgi M (1997), Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Transactions on Consumer Electronics 43(1).
Xie XXX, Shi ZSZ, Guo WGW, Yao SYS (2009), An adaptive image enhancement technique based on image characteristic. 2nd Int. Congress on Image and Signal Processing 1-5.