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A Bayesian/Monte Carlo Segmentation Method for Images Dominated by Gaussian Noise
September 1989 (vol. 11 no. 9)
pp. 985-990

A description is given of a thresholding algorithm that rapidly separates foreground objects from background clutter in images whose dominant feature is zero-mean Gaussian noise. Such images have been found to occur in digital radiography applications in which manufactured parts are imaged by a solid-state camera. The motivation behind the algorithm is discussed in terms of the requirements of an imaging system for nearly-real-time radiography in an industrial environment. The individual steps of the process are described, and the robustness of the technique with respect to signal-to-noise ratio and with respect to object size is discussed.

[1] R. Halmshaw,Industrial Radiography Techniques. London: Wykeham, 1971, ch. 6.
[2] J. S. Weszka, "A survey of threshold selection techniques,"Comput. Graphics Image Processing, vol. 7, pp. 259-265, 1978.
[3] K. S. Fu and J. K. Mui, "A survey on image segmentation,"Pattern Recognition, vol. 13, pp. 3-16, 1981.
[4] R. M. Haralick and L. G. Shapiro, "Image segmentation techniques,"SPIE Appl. Art. Intell. II, vol. 548, pp. 2-9, 1985.
[5] J. Kittler, J. Illingworth, and J. Foglein, "Threshold selection based on a simple image statistic,"Comput. Vision, Graphics, Image Processing, vol. 30, pp. 125-147, 1985.
[6] F. Sadjadi, R. Whillock, and M. Desai, "Image segmentation using an optimum thresholding technique,"Intelligent Robots Comput. Vision: Fifth in a Series, D. P. Casasent, Ed.,Proc. SPIE, vol. 726, pp. 110-114, 1987.
[7] B. Bhanu and O. D. Faugeras, "Segmentation of images having unimodal distributions,"IEEE Trans. Pattern. Anal. Machine Intell., vol. PAMI-4, pp. 408-419, 1982.
[8] A. Rosenfeld, R. Hummel, and S. W. Zucker, "Scene labeling by relaxation operations,"IEEE Trans. Syst., Man, Cybern., vol. SMC-6, pp. 420-433, 1976.
[9] E. L. Hall, R. P. Kruger, S. J. Dwyer, III, D. L. Hall, R. W. McLaren, and G. S. Lodwick, "A survey of preprocessing and feature extraction techniques for radiographic images,"IEEE Trans. Comput., vol. C-20, pp. 1032-1044, 1971.
[10] F. R. Hansen and H. Elliott, "Image segmentation using simple Markov field models,"Comput. Graphics Image Processing, vol. 20, pp. 101-132, 1982.
[11] D. Geman and S. Geman, "Bayesian image analysis," inDisordered Systems and Biological Organization, NATO ASI Series, vol. F20, E. Bienenstock, F. Fogelman-Soulier, and G. Weisbuch, Ed. Berlin: Springer-Verlag, 1986, pp. 301-319.
[12] C. K. Chow and T. Kaneko, "Automatic boundary detection of the left ventricle from cineangiograms,"Comput. Biomed. Res., vol. 5, pp. 388-410, 1972.

Index Terms:
image segmentation; Bayes method; Monte Carlo method; S/N ratio; picture processing; pattern recognition; Gaussian noise; background clutter; digital radiography; Bayes methods; Monte Carlo methods; pattern recognition; picture processing; radiography
Citation:
Z.W. Bell, "A Bayesian/Monte Carlo Segmentation Method for Images Dominated by Gaussian Noise," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 9, pp. 985-990, Sept. 1989, doi:10.1109/34.35502
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