Enhanced PFCM Algorithm for Noisy Image Segmentation Combining Regional and Neighborhood-Level Information

WANG Xiao-peng; WANG Hai-zhou; CHEN Hao-ran

doi:10.12263/DZXB.20241104

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Enhanced PFCM Algorithm for Noisy Image Segmentation Combining Regional and Neighborhood-Level Information

PAPERS | 更新时间：2025-08-18

- Enhanced PFCM Algorithm for Noisy Image Segmentation Combining Regional and Neighborhood-Level Information
- ACTA ELECTRONICA SINICA Vol. 53, Issue 5, Pages: 1584-1595(2025)
- 作者机构：
  
  兰州交通大学电子与信息工程学院，甘肃兰州 730000
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(61761027)
- DOI：10.12263/DZXB.20241104
  CLC： TP391.4;
- Received：09 December 2024，
  
  Revised：2025-04-10，
  
  Published：25 May 2025
- 稿件说明：
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王小鹏, 王海洲, 陈浩然. 区域和邻域级信息相结合的加强型PFCM含噪图像分割算法[J]. 电子学报, 2025, 53(05): 1584-1595.

WANG Xiao-peng, WANG Hai-zhou, CHEN Hao-ran. Enhanced PFCM Algorithm for Noisy Image Segmentation Combining Regional and Neighborhood-Level Information[J]. Acta Electronica Sinica, 2025, 53(05): 1584-1595.
王小鹏, 王海洲, 陈浩然. 区域和邻域级信息相结合的加强型PFCM含噪图像分割算法[J]. 电子学报, 2025, 53(05): 1584-1595. DOI：10.12263/DZXB.20241104

WANG Xiao-peng, WANG Hai-zhou, CHEN Hao-ran. Enhanced PFCM Algorithm for Noisy Image Segmentation Combining Regional and Neighborhood-Level Information[J]. Acta Electronica Sinica, 2025, 53(05): 1584-1595. DOI：10.12263/DZXB.20241104

摘要

针对可能性模糊C均值聚类（Possibilistic Fuzzy C-Means，PFCM）算法存在重合聚类，未考虑图像空间信息，对噪声鲁棒性差的问题，提出一种区域和邻域级信息相结合的加强型可能性模糊C均值算法.首先，设计了一种新的函数结构抑制重合聚类，该方法通过引入非线性衰减特性，更有效地调节不同隶属度点对不同簇的贡献，降低了簇之间的重合度；其次，通过局部方差约束，将图像区域级信息和其邻域级信息结合，充分利用图像的空间信息，提高对噪声的鲁棒性；最后，将核度量应用于聚类不相似度量，根据图像自有信息自适应地确定核函数带宽参数，进一步提高算法的灵活性.含噪合成图像、脑MRI（Magnetic Resonance Imaging）图像和含噪彩色图像分割实验表明，本文算法在分割结果视觉效果和性能评价指标均优于其他几种比较算法.

Abstract

To address the issues of overlapping clusters

lack of spatial information consideration

and poor noise robustness in the possibilistic fuzzy C-Means (PFCM) algorithm

an enhanced PFCM algorithm integrating regional and neighborhood-level information is proposed. First

a novel function structure is designed to suppress overlapping clusters by introducing nonlinear attenuation characteristics

which effectively adjusts the contribution of different membership levels to various clusters

thereby reducing cluster overlap. Second

by incorporating local variance constraints

the algorithm integrates regional and neighborhood-level image information

fully utilizing spatial information to improve noise robustness. Finally

kernel metric is applied to the clustering dissimilarity measure

where the kernel bandwidth parameter is adaptively determined based on the intrinsic properties of the image

further enhancing algorithm flexibility. Segmentation experiments on noisy synthetic images

brain magnetic resonance imaging (MRI)

and noisy color images demonstrate that the proposed algorithm achieves superior visual segmentation results and outperforms existing comparison algorithms in performance evaluation metrics.

关键词

Keywords

references

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