A new criterion for automatic multilevel thresholding

A new criterion for multilevel thresholding is proposed. The criterion is based on the consideration of two factors. The first one is the discrepancy between the thresholded and original images and the second one is the number of bits required to represent the thresholded image. Based on a new maximum correlation criterion for bilevel thresholding, the discrepancy is defined and then a cost function that takes both factors into account is proposed for multilevel thresholding. By minimizing the cost function, the classification number that the gray-levels should be classified and the threshold values can be determined automatically. In addition, the cost function is proven to possess a unique minimum under very mild conditions. Computational analyses indicate that the number of required mathematical operations in the implementation of our algorithm is much less than that of maximum entropy criterion. Finally, simulation results are included to demonstrate their effectiveness.

[1]  Joan S. Weszka,et al.  A survey of threshold selection techniques , 1978 .

[2]  P.K Sahoo,et al.  A survey of thresholding techniques , 1988, Comput. Vis. Graph. Image Process..

[3]  T. W. Ridler,et al.  Picture thresholding using an iterative selection method. , 1978 .

[4]  C. Chow,et al.  Automatic boundary detection of the left ventricle from cineangiograms. , 1972, Computers and biomedical research, an international journal.

[5]  Alfred M. Bruckstein On "Soft" bit allocation , 1987, IEEE Trans. Acoust. Speech Signal Process..

[6]  A W Smeulders,et al.  Application of the method of multiple thresholding to white blood cell classification. , 1988, Computers in biology and medicine.

[7]  R. Gallager Information Theory and Reliable Communication , 1968 .

[8]  King-Sun Fu,et al.  A survey on image segmentation , 1981, Pattern Recognit..

[9]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[10]  Andrew K. C. Wong,et al.  A new method for gray-level picture thresholding using the entropy of the histogram , 1985, Comput. Vis. Graph. Image Process..

[11]  Azriel Rosenfeld,et al.  Histogram modification for threshold selection , 1977 .

[12]  H. R. Keshavan,et al.  An optimal multiple threshold scheme for image segmentation , 1984, IEEE Transactions on Systems, Man, and Cybernetics.

[13]  Bir Bhanu,et al.  Automatic Target Recognition: State of the Art Survey , 1986, IEEE Transactions on Aerospace and Electronic Systems.

[14]  H. Schuster Deterministic chaos: An introduction , 1984 .

[15]  Ahmed S. Abutableb Automatic thresholding of gray-level pictures using two-dimensional entropy , 1989 .

[16]  Reza Safabakhsh,et al.  Computer Vision Techniques for Industrial Applications and Robot Control , 1982, Computer.

[17]  J. R. Parker,et al.  Gray Level Thresholding in Badly Illuminated Images , 1991, IEEE Trans. Pattern Anal. Mach. Intell..