边缘引导和轮廓约束下的跨域香农熵最大化导向的自动阈值选取方法

为了处理诸如高斯、伽马、极值、瑞利、均匀或贝塔等基本灰度分布情形下的阈值选取难题，本文提出了一种跨域香农熵最大化导向的自动阈值选取方法.该方法利用不变的引导边缘图像和变化的约束轮廓图像共同构造出一系列持续变化的一维灰度直方图，并采用香农熵作为熵计算模型，从而得以跨越图像中若干局部区域去计算跨域香农熵，并以最大跨域香农熵对应的阈值作为最终阈值.在40幅合成图像和50幅真实世界图像上的实验结果表明，该方法虽然在计算效率方面不优于Masi熵阈值方法、Tsallis熵阈值方法、局部香农熵阈值方法和迭代三类阈值方法，但在分割适应性方面有显著增强，且在误分割率方面有显著下降.

Automatic Threshold Selection Guided by Maximizing Cross-Region Shannon Entropy Under Edge Guidance and Contour Constraints

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.