Non-Parametric Histogram-Based Thresholding Methods for Weld Defect Detection in Radiography
In non destructive testing by radiography, a perfect
knowledge of the weld defect shape is an essential step to
appreciate the quality of the weld and make decision on its
acceptability or rejection. Because of the complex nature of the
considered images, and in order that the detected defect region
represents the most accurately possible the real defect, the choice
of thresholding methods must be done judiciously. In this paper,
performance criteria are used to conduct a comparative study of
four non parametric histogram thresholding methods for automatic
extraction of weld defect in radiographic images.
[1] L. Soler, G. Malandrin, H. Delinguette, "Segmentation automatique:
Application aux angioscanner 3D", Revue de Traitement de Signal,
vol. 15, 1998.
[2] M. Sezgin, B. Sankur, "Survey over image thresholding techniques
and quantitative performance evaluation". Journal of Electronic
imaging 13(1), Jan. 2004, pp. 146-165.
[3] S.U. Lee, S.Y. Chung, and R.H. Park, "A Comparative Performance
Study of Several Global Thresholding Techniques for Segmentation",
Computer Vision, Graphics, and Image Processing, vol. 52, 1990, pp.
171-190.
[4] N. Otsu, "A Threshold Selection Method from Gray-Level
Histograms", IEEE Trans. on Systems, Man, and Cybern. vol. SMC-
9, 1979, pp. 62-66.
[5] J. Kittler and J. Illingworth, "Minimum Error Thresholding", Pattern
Recognition, 19(1), 1986.
[6] J.N. Kapur, P.K. Sahoo, and A.K.C.Wong, "A New Method for Gray-
Level Picture Thresholding Using the Entropy of the Histogram",
Computer Vision, Graphics, and Image Processing, vol. 29, 1985, pp.
273-285.
[7] W-H. Tsai, "Moment-Preserving Thresholding: A New Approach",
Computer Vision, Graphics, and Image Processing, vol. 29, 1985, pp.
377-393.
[8] Y. J. Zhang, "A survey on evaluation methods for image
segmentation", Pattern Recognition, 29(8), 1996.
[9] W. A. Yasnoff, J. K. Mui, and J. W. Bacus, "Error measures for scene
segmentation", Pattern Recognition, 9, 1977, pp. 217-231.
[10] M. D. Levine and A. M. Nazif, "Dynamic measurement of computer
generated image segmentations", IEEE Trans. Pattern Anal. Mach.
Intell. PAMI-7, 1985, pp. 155-164.
[11] N. Nacereddine, M. Tridi, S. S. Belaïfa, M. Zelmat, "Weld defect
detection in industrial radiography based digital image processing",
International Conference on Signal Processing, ICSP 2004, Istanbul,
Turkey, dec. 2004
[1] L. Soler, G. Malandrin, H. Delinguette, "Segmentation automatique:
Application aux angioscanner 3D", Revue de Traitement de Signal,
vol. 15, 1998.
[2] M. Sezgin, B. Sankur, "Survey over image thresholding techniques
and quantitative performance evaluation". Journal of Electronic
imaging 13(1), Jan. 2004, pp. 146-165.
[3] S.U. Lee, S.Y. Chung, and R.H. Park, "A Comparative Performance
Study of Several Global Thresholding Techniques for Segmentation",
Computer Vision, Graphics, and Image Processing, vol. 52, 1990, pp.
171-190.
[4] N. Otsu, "A Threshold Selection Method from Gray-Level
Histograms", IEEE Trans. on Systems, Man, and Cybern. vol. SMC-
9, 1979, pp. 62-66.
[5] J. Kittler and J. Illingworth, "Minimum Error Thresholding", Pattern
Recognition, 19(1), 1986.
[6] J.N. Kapur, P.K. Sahoo, and A.K.C.Wong, "A New Method for Gray-
Level Picture Thresholding Using the Entropy of the Histogram",
Computer Vision, Graphics, and Image Processing, vol. 29, 1985, pp.
273-285.
[7] W-H. Tsai, "Moment-Preserving Thresholding: A New Approach",
Computer Vision, Graphics, and Image Processing, vol. 29, 1985, pp.
377-393.
[8] Y. J. Zhang, "A survey on evaluation methods for image
segmentation", Pattern Recognition, 29(8), 1996.
[9] W. A. Yasnoff, J. K. Mui, and J. W. Bacus, "Error measures for scene
segmentation", Pattern Recognition, 9, 1977, pp. 217-231.
[10] M. D. Levine and A. M. Nazif, "Dynamic measurement of computer
generated image segmentations", IEEE Trans. Pattern Anal. Mach.
Intell. PAMI-7, 1985, pp. 155-164.
[11] N. Nacereddine, M. Tridi, S. S. Belaïfa, M. Zelmat, "Weld defect
detection in industrial radiography based digital image processing",
International Conference on Signal Processing, ICSP 2004, Istanbul,
Turkey, dec. 2004
@article{"International Journal of Electrical, Electronic and Communication Sciences:63871", author = "N. Nacereddine and L. Hamami and M. Tridi and N. Oucief", title = "Non-Parametric Histogram-Based Thresholding Methods for Weld Defect Detection in Radiography", abstract = "In non destructive testing by radiography, a perfect
knowledge of the weld defect shape is an essential step to
appreciate the quality of the weld and make decision on its
acceptability or rejection. Because of the complex nature of the
considered images, and in order that the detected defect region
represents the most accurately possible the real defect, the choice
of thresholding methods must be done judiciously. In this paper,
performance criteria are used to conduct a comparative study of
four non parametric histogram thresholding methods for automatic
extraction of weld defect in radiographic images.", keywords = "Radiographic images, non parametric methods,
histogram thresholding, performance criteria.", volume = "1", number = "9", pages = "1415-5", }