Optimal multi-level thresholding using a two-stage Otsu optimization approach
- Creators
- Huang, Deng-Yuan1
- Wang, Chia-Hung1
- 1. Department of Electrical Engineering, Da-Yeh University, No. 112, Shanjiao Road, Dacun Township, Changhua County 51505, Taiwan
Description
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 TSMO32, 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.
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