Optimal multi-level thresholding using a two-stage Otsu optimization approach

Otsu’s method of image segmentation selects an optimum threshold by maximizing the between-class variance in a gray image. However, this method becomes very time-consuming when extended to a multi-level threshold problem due to the fact that a large number of iterations are required for computing the cumulative probability and the mean of a class. To greatly improve the efficiency of Otsu’s method, a new fast algorithm called the TSMO method (Two-Stage Multithreshold Otsu method) is presented. The TSMO method outperforms Otsu’s method by greatly reducing the iterations required for computing the between-class variance in an image. The experimental results show that the computational time increases exponentially for the conventional Otsu method with an average ratio of about 76. For TSMO-32, the maximum computational time is only 0.463 s when the class number M increases from two to six with relative errors of less than 1% when compared to Otsu’s method. The ratio of computational time of Otsu’s method to TSMO-32 is rather high, up to 109,708, when six classes (M = 6) in an image are used. This result indicates that the proposed method is far more efficient with an accuracy equivalent to Otsu’s method. It also has the advantage of having a small variance in runtimes for different test images.