多向加权Tsallis熵最大化导向的自动阈值分割方法

邹耀斌; 邓世成; 孟祥丹; 周欢; 孙水发; 陈鹏

doi:10.12263/DZXB.20220540

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多向加权Tsallis熵最大化导向的自动阈值分割方法

学术论文 | 更新时间：2026-04-10

- 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
- 电子学报 2024年52卷第1期页码：129-143
- 作者机构：
  
  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
- 作者简介：
  
  [ "邹耀斌男，1978年生，江西鹰潭人.三峡大学副教授、硕士生导师.主要研究方向为图像处理和相似性理论.E-mail: zyb@ctgu.edu.cn" ]
  [ "邓世成男，1996年生，湖北荆州人.三峡大学硕士研究生.主要研究方向为数字图像处理.E-mail: dscabeginner@163.com" ]
  [ "孟祥丹男，1996年生，湖北十堰人.三峡大学硕士研究生.主要研究方向为数字图像处理.E-mail: mxd19960111@163.com" ]
  [ "周欢男，1986年生，湖北仙桃人.三峡大学教授、博士生导师.主要研究方向为移动社交网络和智能信息处理.中国电子学会会员编号：E190083501M.E-mail: zhouhuan117@163.com" ]
  [ "孙水发男，1977年生，江西黎川人.三峡大学教授、博士生导师.主要研究方向为智能信息处理和计算机视觉.E-mail: sunshuifa1977@yeah.net" ]
  [ "陈鹏男，1973年生，湖北建始人.三峡大学教授、硕士生导师.主要研究方向为智能系统集成和大数据处理与分析.E-mail: chenpeng_ctgu@tom.com" ]
- 基金信息：
  
  国家自然科学基金(62172255;61871258)
- DOI：10.12263/DZXB.20220540
  中图分类号： TP391;
- 收稿：2022-05-12，
  
  修回：2022-10-08，
  
  纸质出版：2024-01-25
- 稿件说明：
移动端阅览
邹耀斌,邓世成,孟祥丹等.多向加权Tsallis熵最大化导向的自动阈值分割方法[J].电子学报,2024,52(01):129-143.

ZOU Yao-bin,DENG Shi-cheng,MENG Xiang-dan,et al.Automatic Thresholding Segmentation Method Guided by Maximizing Multi-Directional Weighted Tsallis Entropy[J].ACTA ELECTRONICA SINICA,2024,52(01):129-143.
邹耀斌,邓世成,孟祥丹等.多向加权Tsallis熵最大化导向的自动阈值分割方法[J].电子学报,2024,52(01):129-143. DOI： 10.12263/DZXB.20220540.

ZOU Yao-bin,DENG Shi-cheng,MENG Xiang-dan,et al.Automatic Thresholding Segmentation Method Guided by Maximizing Multi-Directional Weighted Tsallis Entropy[J].ACTA ELECTRONICA SINICA,2024,52(01):129-143. DOI： 10.12263/DZXB.20220540.

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.

Abstract

Affected by many different factors

such as noise or random details

the size ratio of target to background

or the point spread function during imaging

the gray level histograms of many images appear as non-modal

unimodal

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 different modalities into a unified unimodal right-biased gray level histogram. After extracting this special unimodal right-biased 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 objective 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 method 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 superior to the other six segmentation methods in terms of Matthews correlation coefficient used to quantify segmentation accuracy.

关键词

Keywords

references

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