融入邻域距离与隶属度模糊聚类图像分割算法

朱占龙

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融入邻域距离与隶属度模糊聚类图像分割算法

更新时间：2026-09-04

- 融入邻域距离与隶属度模糊聚类图像分割算法
- Fuzzy Clustering Algorithm by Incorporating Local Distance and Membership for Image Segmentation
- 微电子学与计算机 2018年35卷第4期页码：93-97
- 作者机构：
  
  河北地质大学机构信息工程学院,河北,石家庄,050031
- 作者简介：
- 基金信息：
  
  河北省科技计划项目 17273903D博士科研启动基金 BQ201606
- DOI：
  中图分类号：
- 纸质出版：2018
- 稿件说明：
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朱占龙. 融入邻域距离与隶属度模糊聚类图像分割算法[J]. 微电子学与计算机, 2018, 35(4): 93-97.

ZHU Zhan-long. Fuzzy Clustering Algorithm by Incorporating Local Distance and Membership for Image Segmentation[J]. 2018, 35(4): 93-97.
朱占龙. 融入邻域距离与隶属度模糊聚类图像分割算法[J]. 微电子学与计算机, 2018, 35(4): 93-97. DOI：

ZHU Zhan-long. Fuzzy Clustering Algorithm by Incorporating Local Distance and Membership for Image Segmentation[J]. 2018, 35(4): 93-97. DOI：

摘要

为提升图像分割算法的抗噪性和准确性

提出融入邻域距离与隶属度的模糊聚类算法.首先结合像素邻域灰度信息自适应地对噪声图像进行滤波以构建新的滤波图像

然后在聚类过程中将像素邻域与聚类中心的距离做平滑处理以提高算法的鲁棒性

接着为平衡边界与噪声关系对全局隶属度和邻域隶属度进行结合产生新的权重隶属度函数和新的聚类中心

最后利用加噪的人工合成图像、真实图像进行分割实验.相比其他模糊聚类算法

从定性方面来看

所提算法能够去除更多的噪声点

显示出更好的视觉效果; 从定量方面看

所提算法在分割准确率和ARI指标上有进一步的提升.

Abstract

Due to limitation of the fuzzy clustering algorthm

a new fuzzy clustering algorithm is proposed by incorporating local distance and membership for image segmentation so as to enhance the segmentation accuracy and robustness. Firstly

combining the neighborhood gray information of pixels

the noise image is adaptively filtered to construct a new filtering image

Secondly

the neighborhood distances between local gray values and clustering centers are smoothed for improving the anti-noise capability

and then a weighted membership function

incorporating local and global spatial membership

is emerged to control the tradeoff between boundary and noise

and as a result the new clustering centers is formed. Finally

the obtained segmentation algorithm is carried out on synthetic image

and real images in different levels of noise. The segmentation results not only can reduce the number of spurious blobs and show better visual effects qualitatively

but also demonstrate the higher segmentation accuracy and ARI quantitatively compared with others common fuzzy clustering algorithms.

关键词

Keywords

references

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Ahmed M N, Yamany S M, Mohamed N, et al. A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data[J]. IEEE Transactions on Medical Imaging, 2002, 21(3): 193-199. DOI: 193

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朱然, 李积英. 基于改进的FCM算法图像分割研究[J]. 微电子学与计算机, 2015, 32(6): 151-153. DOI: 151

崔西希, 吴成茂. 核空间中智模糊聚类及图像分割应用[J]. 中国图象图形学报, 2016, 21(10): 1316-1327. DOI: 1316

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