journal article·2019-06

Computer Aided Radiograph Interpretation Tool for Defect Characterization from Weld Plates

K. Sudheera, N. M. Nandhitha

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Abstract

Industrial Radiography is the most widely used NDT technique for weld defect characterization. As it is an indirect technique, an expert radiologist is required at the interpretation end to identify and characterize the defect in radiographs. However radiographs suffer from poor contrast thereby making it difficult to isolate the defect region from the background. Hence it is necessary to develop computer aided analysis tool for automated interpretation of weld radiographs. It involves an efficient contrast enhancement and segmentation technique for isolating the weld defect to its true size. In this work, wavelet based contrast enhancement technique has been developed to highlight the defect region from background. Biorthogonal 6.8 wavelet and Daubechies wavelet of order 4 are used for enhancing root/slag and porosity respectively. Thresholding and region growing are used for segmenting the region of interest.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Ultrasonics and Acoustic Wave Propagation · Welding Techniques and Residual Stresses · Non-Destructive Testing Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (22)

Show source-provided references
1. Sangeetha, M.S. and Nandhitha, N.M., Multilevel thresholding technique for contrast enhancement in thermal images to facilitate accurate image segmentation, Indian J. Sci. Technol., 2016, vol. 9, no. 6. https://doi.org/10.17485/ijst/2016/v9i6/7407510.17485/ijst/2016/v9i6/74075 ↗
2. Irrera, P., Bloch, I., and Delplanque, M., A flexible patch based approach for combined denoising and contrast enhancement of digital X-ray images, Med. Image Anal., 2016, vol. 28, pp. 33–45. https://doi.org/10.1016/j.media.2015.11.00210.1016/j.media.2015.11.002 ↗
3. Se Eun Kim, Jong Ju Jeon, and Il Kyu Eom, Image contrast enhancement using entropy scaling in wavelet domain, Signal Process., 2016, vol. 127, pp. 1–11. https://doi.org/10.1016/j.sigpro.2016.02.01610.1016/j.sigpro.2016.02.016 ↗
4. Yao Nan, Wang KaiSheng, and Cai Yue, Anti-distortion image contrast enhancement algorithm based on fuzzy statistical analysis of the histogram equalization, Int. J. Signal Process., Image Process. Pattern Recogn., 2016, vol. 9, no. 4, pp. 203–114.
5. Mohamad, H., A new image contrast enhancement in fuzzy property domain plane for a true color images, Int. J. Signal Process. Syst., 2016, vol. 4, no. 1. https://doi.org/10.12720/ijsps.4.1.45-5010.12720/ijsps.4.1.45-50 ↗
6. Erdenetogtokh Jamsranjav, Tatsuo Shiina, Kenichi Kuge, Yasuhito Kinjo, Yuichi Nakamura, Kunio Shinohara, and Atsushi Ito, Effect of contrast enhancement prior to iteration procedure on image correction for soft X-ray projection microscopy. http://dx.doi.org/ 2016https://doi.org/10.1063/1.493753110.1063/1.4937531 ↗
7. Al Mehdi Saadat Chowdhury, Image contrast enhancement using tri-histogram equalization based on minimum and maximum intensity occurrence, Curr. Trends Technol. Sci., 2016, vol. 6, no. 2.
8. Sahaya Anselin Nisha and Rahul Kumar Chaurasia, Segmentation of lungs region to detect tuberculosis, in IEEE Sponsored 2nd Int. Conf. Innovations Inf. Embedded Commun. Syst. ICIIECS’15, pp 506–509; Int. J. Appl. Eng. Res., 2015, vol. 10, no. 20, pp. 16 302–16 305.
9. Sudheera, K., Lakshmi Mohanachandran, Nandhitha, N.M., Parithosh Nanekar B. Venkatraman, and Sheela Rani, B., DWT based automated weld pool detection and defect characterisation from weld radiographs, in ICRAMID, 2014, pp. 385–389.10.4028/www.scientific.net/AMR.984-985.573 ↗
10. Movafeghi, A., Using empirical mode decomposition and a fuzzy algorithm for the analysis of weld defect images, Insight Nondestr. Test. Cond. Monit., vol. 57, no. 1, 2015, pp. 35–39(5). https://doi.org/10.1784/insi.2014.57.1.3510.1784/insi.2014.57.1.35 ↗
11. Amir Movafeghi, Effat Yahaghi, and Noureddin Mohammadzadeh, Defect detection improvement of digitised radiographs by principal component analysis with local pixel grouping, J. Nondestr. Eval., 2015, vol. 34, no. 17. https://doi.org/10.1007/s10921-015-0290-z10.1007/s10921-015-0290-z ↗
12. Tian Song, Zhijiang Li, and Liqin Cao, Improved local adaptive image enhancement algorithm based on lee algorithm, Adv. Graphic Commun. Packag. Technol. Mater., vol. 369 of Lect. Notes Electr. Eng., 2015, pp. 203–209.10.1007/978-981-10-0072-0_27 ↗
13. Changying Dang, Jianmin Gao, Zhao Wang, Fumin Chen, and Yulin Xiao, Multi-step radiographic image enhancement conforming to weld defect segmentation, IET Image Process., vol. 9, no. 11. https://doi.org/10.1049/iet-ipr.2014.071610.1049/iet-ipr.2014.0716 ↗
14. Sornam, M. and Hariprabha, A., A method to improve image enhancement through image fusion using wavelet, in 2014 Int. Conf. Intell. Comput. Appl. https://doi.org/10.1109/ICICA.2014.6310.1109/ICICA.2014.63 ↗
15. Koushlendra K. Singh, Rajesh K. Pandey, and Suraj Suman, Contrast enhancement using lifting wavelet transform, in Int. Conf. Control Instrum. Commun. Comput. Technol. (ICCICCT).
16. Nitin Shanna and Om Prakash Venna, Gamma correction based satellite image enhancement using singular value decomposition and discrete wavelet transform, in 2014 IEEE Int. Conf. Adv. Commun. Control Comput. Technol. (ICACCCT).
17. Ming Zenga, Youfu Li, Qinghao Menga, Ting Yanga, and Jian Liua, Improving histogram-based image contrast enhancement using gray-level information histogram with application to X-ray images. Optik, 2012, vol. 123, pp. 511–520. https://doi.org/10.1016/j.ijleo.2011.05.01710.1016/j.ijleo.2011.05.017 ↗
18. Anita Pati, Priti Gupta, Vivek Chandra Mishra, and Dhiresh Kumar Pathak, A new developed fuzzy inference technique for image enhancement, Int. J. Res. Comput. Appl. Rob., 2015, vol. 3, no. 2, pp. 98–104.
19. Neury Boaretto and Tania Mezzadri Centeno, Automated detection of welding defects in pipelines from radiographic images DWDI, NDT & E Int., 2017, vol. 86, pp. 7–13.10.1016/j.ndteint.2016.11.003 ↗
20. Benzhi Chen, Zhihong Fang, Yong Xia, Ling Zhang, Yijie Huang, and Lisheng Wang, Accurate defect detection via sparsity reconstruction for weld radiographs, NDT & E Int., 2018, vol. 94, pp. 62–69.10.1016/j.ndteint.2017.11.006 ↗
21. Yirong Zou, Dong Du, Baohua Chang, Linhong Ji, and Jiluan, Pan, Automatic weld defect detection method based on Kalman filtering for real-time radiographic inspection of spiral pipe, NDT & E Int., 2015, vol. 72, pp. 1–9.10.1016/j.ndteint.2015.01.002 ↗
22. Zahran, O., Kasban, H., El-Kordy, M., and AbdEl-Samie, F.E., Automatic weld defect identification from radiographic images, NDT & E Int., 2013, vol. 57, pp. 26–35.10.1016/j.ndteint.2012.11.005 ↗