多向加权Tsallis熵最大化导向的自动阈值分割方法  

Automatic Thresholding Segmentation Method Guided by Maximizing Multi-Directional Weighted Tsallis Entropy

作　　者：邹耀斌[1,2,3] 邓世成 孟祥丹 周欢[2,3] 孙水发[1,3] 陈鹏[2,3] ZOU Yao-bin;DENG Shi-cheng;MENG Xiang-dan;ZHOU Huan;SUN Shui-fa;CHEN Peng(Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering,(China Three Gorges University),Yichang,Hubei 443002,China;Center for Big Data,China Three Gorges University,Yichang,Hubei 443002,China;College of Computer and Information Technology,China Three Gorges University,Yichang,Hubei 443002,China)

机构地区：[1]水电工程智能视觉监测湖北省重点实验室(三峡大学),湖北宜昌443002 [2]三峡大学大数据研究中心,湖北宜昌443002 [3]三峡大学计算机与信息学院,湖北宜昌443002

出　　处：《电子学报》2024年第1期129-143,共15页Acta Electronica Sinica

基　　金：国家自然科学基金(No.62172255,No.61871258)。

摘　　要：受噪声或随机细节、目标和背景的大小比例、成像时的点扩散等不同因素的影响,许多图像的灰度直方图呈现为无模态、单模态、双模态或者多模态样式.为了在统一框架内处理这4种不同模态情形下的自动阈值选择问题,本文提出了一种多向加权Tsallis熵最大化导向的自动阈值分割方法(Multi-directional Weighted Tsallis Entropy,MWTE).基于新设计的反正切方向性卷积核的多尺度乘积效应,该方法将不同模态的灰度直方图转化为统一的单模态右偏灰度直方图.在4个不同方向上提取出这种特殊的单模态右偏灰度直方图后,通过多向加权策略构建出与原始图像灰度值紧密相关的加权Tsallis熵目标函数,并以该目标函数取最大值时对应的灰度值作为最终分割阈值.本文将提出的方法和3个阈值分割方法、1个软分割方法、1个活动轮廓分割方法以及1个自动聚类分割方法进行了比较.在4种不同模态情形下的4幅合成图像和50幅真实世界图像上的实验结果表明,本文提出的方法虽然在计算效率方面不占有优势,但它对不同模态的测试图像具有更稳健的分割适应性,且在量化分割精度所用的马修斯相关系数方面优于其他6个分割方法.Affected by many different factors,such as noise or random details,the size ratio of target to back-ground,or the point spread function during imaging,the gray level histograms of many images appear as non-modal,uni-modal,bimodal or multimodal patterns.To deal with the issue of automatic threshold selection in these four different modal situations within a unified framework,an automatic thresholding segmentation method guided by maximizing the multi-directional weighted Tsallis entropy is proposed in this paper.Based on the multi-scale product effect of a newly designed arctangent directional convolution kernel,the proposed method first converts the gray level histograms of differ-ent modalities into a unified unimodal right-biased gray level histogram.After extracting this special unimodal right-bi-ased gray level histogram in four different directions,a multi-directional weighted strategy is utilized to construct a weighted Tsallis entropy objective function that is closely related to the gray levels of the original image.When the ob-jective function takes the maximum value,the corresponding gray level is used as the final segmentation threshold.The proposed method is compared with three thresholding methods,one soft segmentation method,one active contour meth-od and one automatic clustering segmentation method.The experimental results on four synthetic images and fifty real-world images in four different modal situations show that although the proposed method has no advantage in terms of computational efficiency,it has more robust segmentation adaptability to test images of different modalities,and it is su-perior to the other six segmentation methods in terms of Matthews correlation coefficient used to quantify segmentation accuracy.

关 键 词：阈值分割 Tsallis熵差 加权Tsallis熵 反正切方向性卷积核 多尺度乘积效应 马修斯相关系数 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]