A threshold selection method from gray level histograms

A nonparametric and unsupervised method ofautomatic threshold selection for picture segmentation is presented. An optimal threshold is selected by the discriminant criterion, namely, so as to maximize the separability of the resultant classes in gray levels. The procedure is very simple, utilizing only the zerothand the first-order cumulative moments of the gray-level histogram. It is straightforward to extend the method to multithreshold problems. Several experimental results are also presented to support the validity of the method.

[1]  Robert W. Sittler,et al.  An Optimal Data Association Problem in Surveillance Theory , 1964, IEEE Transactions on Military Electronics.

[2]  M L Mendelsohn,et al.  THE ANALYSIS OF CELL IMAGES * , 1966, Annals of the New York Academy of Sciences.

[3]  Howard S. White.  Finding Events in a Sea of Bubbles , 1971, IEEE Transactions on Computers.

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

[5]  Sadakazu Watanabe,et al.  An automated apparatus for cancer prescreening: CYBEST , 1974, Comput. Graph. Image Process..

[6]  Azriel Rosenfeld,et al.  A Threshold Selection Technique , 1974, IEEE Transactions on Computers.

[7]  P. Smith,et al.  A branching algorithm for discriminating and tracking multiple objects , 1975 .

[8]  C. Morefield.  Application of 0-1 integer programming to a track assembly problem , 1975, 1975 IEEE Conference on Decision and Control including the 14th Symposium on Adaptive Processes.

[9]  Patrick L. Smith.  Reduction of Sea Surveillance Data Using Binary Matrices , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[10]  G. Redinbo.  Book reviews - Orthogonal transforms for digital signal processing , 1977, IEEE Communications Society Magazine.