Performance of noise removal methods with image quality parameter on μ-focused digital radiographic image

This study deals with noise removal methods on radiographic image which acquired using μ-focused digital radiography machine. The purposes of the study are to enhance the quality of radiographic image using noise removal methods and analyze the image quality using error measurement metrics. Median, gaussian, average and circular averaging filters are the noise removal methods applied on original radiographic image to produce processed image. Then, the processed image are measured in terms of Signal to Noise Ratio (SNR), Maximum Absolute Error (MAXABS), Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Root Mean Square Error (RMSE). Besides that, the image quality is also measured using Modulation Transfer Function (MTF). Results show that gaussian filter gives the best enhancement of image quality based on error metrics and MTF. The development of image enhancement and quality measurement methods implementation are done using MATLAB R2009a.

[1]  M L Giger,et al.  Investigation of basic imaging properties in digital radiography. 6. MTFs of II-TV digital imaging systems. , 1985, Medical physics.

[2]  Uwe Ewert,et al.  Strategies for Film Replacement in Radiography - a comparative study - , 2007 .

[3]  Juan Zapata-Pérez,et al.  An Automatic Welding Defects Classifier System , 2008, VISAPP.

[4]  M. Zelmat,et al.  Weld Defect Detection in Industrial Radiography Based Digital Image Processing , 2007 .

[5]  A. R. Hamzah,et al.  Preliminary Modulation Transfer Function Study on Amorphous Silicon Flat Panel System for Industrial Digital Radiography , 2022 .

[6]  Abdul Rahman Ramli,et al.  A comparative study of image compression between JPEG and wavelet , 2001 .

[7]  Juan Zapata-Pérez,et al.  Performance evaluation of an automatic inspection system of weld defects in radiographic images based on neuro-classifiers , 2011, Expert Syst. Appl..

[8]  Domingo Mery,et al.  The state of the art of weld seam radiographic testing Part I, image processing , 2007 .

[9]  R. J. Pardikar.  Digital radiography and Computed radiography for Enhancing the Quality and Productivity of Weldments in Boiler components , 2008 .

[10]  Arsmah Ibrahim,et al.  The geometrical feature of weld defect in assessing digital radiographic image , 2011, 2011 IEEE International Conference on Imaging Systems and Techniques.

[11]  Qian Huang,et al.  Improving Automatic Detection of Defects in Castings by Applying Wavelet Technique , 2006, IEEE Transactions on Industrial Electronics.

[12]  S. Margret Anouncia,et al.  Automatic Defect Detection and Counting In Radiographic Weldment Images , 2010 .

[13]  Li Minxia,et al.  A Study of Automatic Defects Extraction of X-ray Weld Image Based on Computed Radiography System , 2011, 2011 Third International Conference on Measuring Technology and Mechatronics Automation.

[14]  L. Rangarajan,et al.  Enhancing Radiographic Images Using Two Dimensional Left Median Filter , 2011, 2011 Third National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics.

[15]  O. M. Rijal,et al.  Functional quality and performance metric for some image processing applications , 2022 .

[16]  Domingo Mery,et al.  State-ofthe-Art of Weld Seam Inspection by Radiographic Testing : Part I – Image Processing , 2007 .

[17]  Pradeep Kumar,et al.  Flaw detection in radiographic weld images using morphological approach , 2006 .

[18]  Pradeep Kumar,et al.  Flaw detection in radiographic weldment images using morphological watershed segmentation technique , 2009 .

[19]  Uwe Ewert,et al.  Possibilities and Limits of Digital Industrial Radiology: - The new high contrast sensitivity technique - Examples and system theoretical analysis , 2007 .