Digital Radiography Image Quality: Image Processing and Display

2007 ◽  
Vol 4 (6) ◽  
pp. 389-400 ◽  
Author(s):  
Elizabeth A. Krupinski ◽  
Mark B. Williams ◽  
Katherine Andriole ◽  
Keith J. Strauss ◽  
Kimberly Applegate ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Digital Radiography ◽  
Quality Image

scholarly journals Development of Stand Alone Application Tool for Processing and Quality Measurement of Weld Imperfection Image Captured by μ-Focused Digital Radiography Using MATLAB- Based Graphical User Interface

2012 ◽  
Vol 9 (2) ◽  
pp. 64 ◽  
Author(s):  
PZ Nadila ◽  
YHP Manurung ◽  
SA Halim ◽  
SK Abas ◽  
G Tham ◽  
...  
Keyword(s):  
Image Processing ◽  
User Interface ◽  
Image Quality ◽  
Digital Radiography ◽  
Graphical User Interface ◽  
Signal To Noise Ratio ◽  
Quality Measurement ◽  
Noise Removal ◽  
Signal To Noise ◽  
Noise Ratio

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. 

Digital Radiography Image Quality: Image Acquisition

2007 ◽  
Vol 4 (6) ◽  
pp. 371-388 ◽  
Author(s):  
Mark B. Williams ◽  
Elizabeth A. Krupinski ◽  
Keith J. Strauss ◽  
William K. Breeden ◽  
Mark S. Rzeszotarski ◽  
...  
Keyword(s):  
Image Quality ◽  
Digital Radiography ◽  
Image Acquisition ◽  
Quality Image

Detection Devices and Technologies on Large-Scale Pipe Weld Surface Defect

2012 ◽  
Vol 580 ◽  
pp. 445-448
Author(s):  
Qi Liu ◽  
Yu Lan Wei ◽  
Bing Li ◽  
Meng Dan Jin ◽  
Ying Ying Fan
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Defect Detection ◽  
Large Scale ◽  
Surface Defect ◽  
Time System ◽  
Real Time System ◽  
Weld Defect ◽  
Quality Image ◽  
Enhancement Method

The kind and extent of defect can be identified through image processing. First, the weld defect detection device should be constructed, and then the defect imaged should be obtained through rational way, in order to enhance the image quality, image filter and image enhancement method should be use. To ensure the real-time system, the weld region need to segment from the image. After that, the needed defect features need to determine and extract. Finally, the kind, the location and the size of the defect can be defined.

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