This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
A Spatial Thresholding Method for Image Segmentation
November 1988 (vol. 10 no. 6)
pp. 919-927

Several model-based algorithms for threshold selection are presented, concentrating on the two-population univariate case in which an image contains an object and background. It is shown how the main ideas behind two important nonspatial thresholding algorithms follow from classical discriminant analysis. Novel thresholding algorithms that make use of available local/spatial information are then given. It is found that an algorithm using alternating mean thresholding and median filtering provides an acceptable method when the image is relatively highly contaminated, and seems to depend less on initial values than other procedures. The methods are also applicable to multispectral k-population images.

[1] T. W. Ridler and S. Calvard, "Picture thresholding using an iterative selection method,"IEEE Trans. Syst., Man., Cybern., vol. SMC-8, pp. 630-632, Aug. 1978.
[2] D. E. Lloyd, "Automatic target classification using moment invariants of image shapes," Farnborough, UK, Rep. RAE IDN AW126. Dec. 1985.
[3] S. M. Dunn, D. Harwood, and L. S. Davis, "Local estimation of the uniform error threshold,"IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-6, pp. 742-747, 1984.
[4] P. Switzer, "Some spatial statistics for the interpretation of satellite data" (with discussion),Bull. Int. Statist. Inst., vol. 50, pp. 962-972 (book 2), pp. 409-426 (book 3). 1983.
[5] K. V. Mardia, "Spatial discrimination and classification maps,"Commun. Statist. Theor. Meth., vol. 13, no. 18, pp. 2181-2197, 1984.
[6] J. Besag, "On the statistical analysis of dirty pictures" (with discussion),J. Roy. Sfatist. Soc. B, vol. 48, no. 3, pp. 259-302, 1986.
[7] K. V. Mardia, J. T. Kent, J. M. Bibby,Multivariate Analysis. London: Academic, 1979.
[8] R. O. Duda and P. E. Hart,Pattern Classification and Scene Analysis. New York: Wiley, 1973.
[9] F. R. D. Velasco, "Thresholding using the ISODATA clustering algorithm,"IEEE Trans. Syst., Man., Cybern., vol. SMC-10, pp. 771- 774, Nov. 1980.
[10] J. Kittler and J. Illingworth, "On threshold selection using clustering criteria,"IEEE Trans. Syst., Man., Cybern., vol. SMC-15, pp. 652- 655. Sept.-Oct. 1985.
[11] S. Dunn, L. Janos, and A. Rosenfeld, "Bimean clustering,"Pattern Recognition Lett., vol. 1, pp. 169-173, Mar. 1983.
[12] J. Kittler and J. Föglein, "Contextual classification of multispectral pixel data,"Image and Vision Comput., vol. 2, no. 1, pp. 13-29, Feb. 1984.
[13] P. Switzer, "Extension of linear discriminant analysis for statistical classification of remotely sensed satellite imagery,"J. Int. Ass. Math. Geol., vol. 12, pp. 367-376, 1980.
[14] M. David,Geostatistical Ore Reserve Estimation. New York: Elsevier, 1977.
[15] A. Rosenfeld and A. Kak,Digital Picture Processing, New York: Academic, 1976.
[16] S. Geman and D. Geman, "Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,"IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-6, pp. 721-741, 1984.
[17] N. Otsu, "A threshold selection method from grey-level histograms,"IEEE Trans. Syst., Man., Cybern., vol. SMC-9, pp. 62-66, Jan. 1979.

Index Terms:
computerized picture processing; spatial thresholding method; image segmentation; model-based algorithms; median filtering; multispectral k-population images; computerised picture processing; filtering and prediction theory
Citation:
K.V. Mardia, T.J. Hainsworth, "A Spatial Thresholding Method for Image Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 10, no. 6, pp. 919-927, Nov. 1988, doi:10.1109/34.9113
Usage of this product signifies your acceptance of the Terms of Use.