journal article·2023-09

Hybrid Level Set Driven by Signed Pressure Force for Welding Defects Segmentation

Mohammed Khorchef, Naim Ramou, Rafik Bradai, Yamina Boutiche, Nabil Chetih

Publisher page

Abstract

This research paper focuses on the segmentation of welding defects in radiographic images, which plays a crucial role in the diagnosis and monitoring of defects in the industry. Our primary challenge is to develop a method that incorporates geodesic active contours for local segmentation and specifically targets the segmentation of welding defects regions in radiographic images used for non-destructive control and analysis. The objective of this study is to assess the robustness of our proposed method on various radiographic images. Our findings indicate that the proposed method is not only robust but also highly effective.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Welding Techniques and Residual Stresses · Non-Destructive Testing Techniques · Image and Object Detection Techniques

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

References (17)

Show source-provided references
1. Ayache, N., Cohen, I., and Herlin, I., Medical Image Tracking, Cambridge: MIT Press, 1992.
2. Horritt, M., A statistical active contour model for SAR image segmentation, Imag. Vision Comput., 1999, vol. 17, nos. 3–4, pp. 213–224. https://doi.org/10.1016/S0262-8856(98)00101-210.1016/S0262-8856(98)00101-2 ↗
3. Smith, S.M., ASSET-2: Real-time motion segmentation and object tracking, Real-Time Imag., 1998, vol. 4, no. 1, pp. 21–40. https://doi.org/10.1006/rtim.1996.006110.1006/rtim.1996.0061 ↗
4. Ramou, N., Segmentation of weld defects using multiphase level set by the piecewise-smooth Mumford–Shah model, Russ. J. Nondestr. Test., 2019, vol. 55, no. 2, pp. 155–161. https://doi.org/10.1134/S106183091902007410.1134/S1061830919020074 ↗
5. Mumford, D.B. and Shah, J., Optimal approximations by piecewise smooth functions and associated variational problems, Commun. Pure Appl. Math., 1989, vol. 42, no. 5. https://doi.org/10.1002/cpa.316042050310.1002/cpa.3160420503 ↗
6. Osher, S. and Sethian, J.A., Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations, J. Comput. Phys., 1988, vol. 79, no. 1, pp. 12–49. https://doi.org/10.1016/0021-9991(88)90002-210.1016/0021-9991(88)90002-2 ↗
7. Malladi, R., Sethian, J.A., and Vemuri, B.C., Shape modeling with front propagation: A level set approach, IEEE Trans. Pattern Anal. Mach. Intel., 1995, vol. 17, no. 2, pp. 158–175. https://doi.org/10.1109/34.36817310.1109/34.368173 ↗
8. Zhu, S.C. and Yuille, A., Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation, IEEE Trans. Pattern Anal. Mach. Intel., 1996, vol. 18, no. 9, pp. 884–900. https://doi.org/10.1109/34.53734310.1109/34.537343 ↗
9. Weese, J., Kaus, M., Lorenz, C., Lobregt, S., Truyen, R., and Pekar, V., Shape constrained deformable models for 3D medical image segmentation, Inform. Proces. Med. Imag. 17th Int. Conf. (Davis, 2001), pp. 380–387. https://doi.org/10.1007/3-540-45729-1_3810.1007/3-540-45729-1_38 ↗
10. Chan, T.F. and Vese, L.A., Image segmentation using level sets and the piecewise-constant Mumford–Shah model, UCLA CAM Report 00-14, 2000, pp. 1–24.
11. Leventon, M.E., Faugeras, O., Grimson, W.E.L., and Wells, W.M., Level set based segmentation with intensity and curvature priors, Proc. IEEE Workshop Math. Meth. Biomed. Imag. Anal., 2000, pp. 4–11. https://doi.org/10.1109/MMBIA.2000.85235410.1109/MMBIA.2000.852354 ↗
12. Halimi, M. and Ramou, N., Extraction of weld defects dimension from radiographic images using the level set segmentation without re-initialization, Russ. J. Nondestr. Test., 2013, vol. 49, pp. 424–429. https://doi.org/10.1109/ICCAT.2013.652199810.1109/ICCAT.2013.6521998 ↗
13. Chan, T.F. and Vese, L.A., Active contours without edges, IEEE Trans. Imag. Process., 2001, vol. 10, no. 2, pp. 266–277. https://doi.org/10.1109/83.90229110.1109/83.902291 ↗
14. Kim, J., Fisher, J. W., Yezzi, A., Çetin, M., and Willsky, A.S., A nonparametric statistical method for image segmentation using information theory and curve evolution, IEEE Trans. Imag. Process., 2005, vol. 14, no. 10, pp. 1486–1502. https://doi.org/10.1109/TIP.2005.85444210.1109/TIP.2005.854442 ↗
15. Girolami, M., Mercer kernel-based clustering in feature space, IEEE Trans. Neural Networks, 2002, vol. 13, no. 3, pp. 780–784. https://doi.org/10.1109/TNN.2002.100015010.1109/TNN.2002.1000150 ↗
16. Zhang, D. and Chen, S., Fuzzy clustering using kernel method, 2002 Int. Conf. Control Autom. (Xiamen, 2002), pp. 162–163. https://doi.org/10.1109/ICCA.2002.122953510.1109/ICCA.2002.1229535 ↗
17. Chen, S. and Zhang, D., Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure, IEEE Trans. Syst. Man Cybern. B., 2004, vol. 34, no. 4, pp. 1907–1916. https://doi.org/10.1109/TSMCB.2004.83116510.1109/TSMCB.2004.831165 ↗