Automatic Image Thresholding Based on Shannon Entropy Difference and Dynamic Synergic Entropy

Abstract

An automatic thresholding method based on Shannon entropy difference and dynamic synergic entropy is proposed to select a reasonable threshold from the gray level image with a unimodal, bimodal, multimodal, or peakless gray level histogram. Firstly, a new concept called Shannon entropy difference is proposed, and the stopping condition of a multi-scale multiplication transformation is automatically controlled by maximizing Shannon entropy difference to produce edge images. Secondly, the gray level image is thresholded by the gray levels in order from smallest to largest to generate a series of binary images, followed by extracting contour images from the binary images. Then, a series of gray level histograms that can dynamically reflect gray level distributions and pixel positions are constructed using the edge images and the contour images synergically. Finally, dynamic synergic Shannon entropy is calculated from this series of gray level histograms, and the threshold corresponding to maximum dynamic synergic entropy is taken as the final segmentation threshold. The experimental results on 40 synthetic images and 50 real-world images show that, although the proposed method is not superior to 8 automatic segmentation methods in computational efficiency, it has more flexible adaptivity of selecting threshold and better segmentation accuracy. INDEX TERMS Automatic thresholding, Principle of maximum entropy, Shannon entropy difference, Dynamic synergic entropy.

Self-Adaptive Image Thresholding within Nonextensive Entropy and the Variance of the Gray-Level Distribution

“…In order to show the advantages of the proposed algorithm more convincingly, we choose 50 test images from VOC-2012, BSD300, and Ref. [45] to compare the performance of these five algorithms. These images have totally different gray-level histograms.…”

Transfer learning‐based surrogate‐assisted design optimisation of a five‐phase magnet‐shaping PMSM

“…Figure 16 shows the probability density distribution and Gauss fitting results of three objectives. The Shannon Entropy [43] and signal-to-noise ratio (Nominal is best) are used to check the robustness of three objectives; Shannon Entropy and signal-to-noise ratio (Nominal is best) can be calculated as Equation (36) and Equation (37), where μ and σ are the mean value and the standard deviation, respectively. The robustness comparison results are given in Table 7.…”

Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation

“…Therefore, researchers have combined various optimization algorithms, such as swarm intelligence, into image segmentation models to improve the efficiency of obtaining threshold sets. For example, Zou et al [ 22 ] presented an enhanced thresholding model with Shannon entropy difference and dynamic synergic entropy, and they applied this to MLIS. Zhao et al [ 23 ] proposed an adaptive thresholding segmentation model based on a multiobjective artificial bee colony optimizer that combines the between-class variance function and the interval-valued fuzzy entropy function.…”