Automatic Threshold Selection Guided by Maximizing Cross-Region Shannon Entropy Under Edge Guidance and Contour Constraints
ZOU Yao-bin1, QIAO Yan2, SUN Shui-fa1, ZANG Zhao-xiang1, XIA Ping1, WANG Jun-ying1, DONG Fang-min1, GONG Guo-qiang1
1. College of Computer and Information Technology, China Three Gorges University, Yichang, Hubei 443002, China;
2. School of Information and Computer, Anhui Agricultural University, Hefei, Anhui 230036, China
Abstract：When the basic distribution constituting one gray level histogram is presented as a non-Gaussian distribution,such as gamma,extreme value,Rayleigh,uniform or beta distribution,how to automatically select the best possible segmentation threshold is still quite challenging.To deal with the issue of threshold selection in the above-mentioned different gray level distributions,we propose an automatic method of threshold selection that is guided by maximizing cross-region Shannon entropy under edge guidance and contour constraints.This method utilizes constant guiding edges and dynamically changing contours to construct a series of continuously changing one-dimensional gray level histograms,and adopts Shannon entropy as the entropy calculation model.Therefore,it can calculate the cross-region Shannon entropy across several local regions in the image,and it takes the threshold corresponding to the maximum cross-region Shannon entropy as the final segmentation threshold.The proposed method is compared with Masi entropy thresholding,Tsallis entropy thresholding,Shannon entropy thresholding,and iterative triclass thresholding on 40 synthetic images and 50 real-world images.The results show that the proposed method is not superior to the 4 compared methods in computational efficiency,but it has significant enhancement in segmentation adaptability and a significant decrease in the mis-segmentation rate.
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2019, Vol. 47 
