区域和邻域级信息相结合的加强型PFCM含噪图像分割算法

王小鹏; 王海洲; 陈浩然

doi:10.12263/DZXB.20241104

您当前的位置：

首页 >

文章列表页 >

区域和邻域级信息相结合的加强型PFCM含噪图像分割算法

学术论文 | 更新时间：2025-08-18

- 区域和邻域级信息相结合的加强型PFCM含噪图像分割算法
- Enhanced PFCM Algorithm for Noisy Image Segmentation Combining Regional and Neighborhood-Level Information
- 电子学报 2025年53卷第5期页码：1584-1595
- 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：
  
  [ "王小鹏男，1969年4月生，甘肃兰州人.兰州交通大学电子与信息工程学院教授和博士生导师.主要研究方向为图像处理与分析、智能化信息处理等. E-mail: Wangxiaopeng@mail.lzjtu.cn" ]
  [ "王海洲男，1998年8月生，甘肃临夏人.兰州交通大学电子与信息工程学院硕士研究生.主要研究方向为图像处理、模式识别. E-mail: 361411775@qq.com" ]
  [ "陈浩然男，2000年7月生，北京人.兰州交通大学电子与信息工程学院硕士研究生.主要研究方向为图像处理. E-mail: saika707@outlook.com" ]
- 基金信息：
  
  国家自然科学基金(61761027)
- DOI：10.12263/DZXB.20241104
  中图分类号： TP391.4;
- 收稿：2024-12-09，
  
  修回：2025-04-10，
  
  纸质出版：2025-05-25
- 稿件说明：
移动端阅览
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

HU G , ZHENG Y X , HOUSSEIN E H , et al . GSRPSO: A multi-strategy integrated particle swarm algorithm for multi-threshold segmentation of real cervical cancer images [J ] . Swarm and Evolutionary Computation , 2024 , 91 : 101766 .

YANG D P , PENG B , AL-HUDA Z , et al . An overview of edge and object contour detection [J ] . Neurocomputing , 2022 , 488 : 470 - 493 .

房超 , 王小鹏 , 李宝民 , 等 . 基于自适应结构元素的改进分水岭图像分割方法 [J ] . 哈尔滨工业大学学报 , 2023 , 55 ( 5 ): 59 - 70 .

FANG C , WANG X P , LI B M , et al . Improved watershed image segmentation method based on adaptive structural elements [J ] . Journal of Harbin Institute of Technology , 2023 , 55 ( 5 ): 59 - 70 . (in Chinese)

吴嘉昕 , 王小鹏 , 刘扬洋 . 子空间与KL信息结合的FCM多光谱遥感图像分割 [J ] . 湖南大学学报(自然科学版) , 2024 , 51 ( 8 ): 23 - 33 .

WU J X , WANG X P , LIU Y Y . Fuzzy C-means multi-spectral remote sensing image segmentation with combined subspace and KL information [J ] . Journal of Hunan University (Natural Sciences) , 2024 , 51 ( 8 ): 23 - 33 . (in Chinese)

孙灏铖 , 刘力 , 李凡长 . 李群模糊C均值聚类图像分割算法 [J ] . 软件学报 , 2024 , 35 ( 10 ): 4806 - 4825 .

SUN H C , LIU L , LI F C , et al . Lie group fuzzy C-means clustering algorithm for image segmentation [J ] . Journal of Software , 2024 , 35 ( 10 ): 4806 - 4825 . (in Chinese)

HATHAWAY R J , BEZDEK J C . Recent convergence results for the fuzzy C-means clustering algorithms [J ] . Journal of Classification , 1988 , 5 ( 2 ): 237 - 247 .

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 .

CHEN S C , ZHANG D Q . Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure [J ] . IEEE Transactions on Systems, Man, and Cybernetics , Part B (Cybernetics), 2004 , 34 ( 4 ): 1907 - 1916 .

BARRAH H , CHERKAOUI A , SARSRI D . Robust FCM algorithm based on local noise detecting for image segmentation [J ] . Advances in Fuzzy Systems , 2016 , 12 ( 21 ): 1 - 10 .

KRINIDIS S , CHATZIS V . A robust fuzzy local information C-means clustering algorithm [J ] . IEEE Transactions on Image Processing , 2010 , 19 ( 5 ): 1328 - 1337 .

SZILAGYI L , BENYO Z , SZILAGYI S M , et al . MR brain image segmentation using an enhanced fuzzy C-means algorithm [C ] // 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society . Piscataway : IEEE , 2003 : 724 - 726 .

CAI W L , CHEN S C , ZHANG D Q . Fast and robust fuzzy C-means clustering algorithms incorporating local information for image segmentation [J ] . Pattern Recognition , 2007 , 40 ( 3 ): 825 - 838 .

LEI T , JIA X H , ZHANG Y N , et al . Significantly fast and robust fuzzy C-means clustering algorithm based on morphological reconstruction and membership filtering [J ] . IEEE Transactions on Fuzzy Systems , 2018 , 26 ( 5 ): 3027 - 3041 .

LEI T , JIA X H , ZHANG Y N , et al . Superpixel-based fast fuzzy C-means clustering for color image segmentation [J ] . IEEE Transactions on Fuzzy Systems , 2019 , 27 ( 9 ): 1753 - 1766 .

GONG M G , LIANG Y , SHI J , et al . Fuzzy C-means clustering with local information and kernel metric for image segmentation [J ] . IEEE Transactions on Image Processing , 2013 , 22 ( 2 ): 573 - 584 .

ELAZAB A , WANG C M , JIA F C , et al . Segmentation of brain tissues from magnetic resonance images using adaptively regularized kernel-based fuzzy C-means clustering [J ] . Computational and Mathematical Methods in Medicine , 2015 , 2015 ( 1 ): 485495 .

FAROOQ A , MEMON K H . Kernel possibilistic fuzzy C-means clustering algorithm based on morphological reconstruction and membership filtering [J ] . Fuzzy Sets and Systems , 2024 , 477 : 108792 .

ZHAO F , JIAO L C , LIU H Q . Fuzzy C-means clustering with non local spatial information for noisy image segmentation [J ] . Frontiers of Computer Science in China , 2011 , 5 ( 1 ): 45 - 56 .

WEI T Y , WANG X P , LI X N , et al . Fuzzy subspace clustering noisy image segmentation algorithm with adaptive local variance & non-local information and mean membership linking [J ] . Engineering Applications of Artificial Intelligence , 2022 , 110 : 104672 .

王小鹏 , 魏统艺 , 房超 , 等 . 自适应非局部空间约束与K-L信息的模糊C-均值噪声图像分割算法 [J ] . 控制理论与应用 , 2022 , 39 ( 7 ): 1261 - 1271 .

WANG X P , WEI T Y , FANG C , et al . Fuzzy C-means algorithm with adaptive non-local spatial constraints and K-L information for noisy image segmentation [J ] . Control Theory & Applications , 2022 , 39 ( 7 ): 1261 - 1271 . (in Chinese)

VERMA H , AGRAWAL R K , SHARAN A . An improved intuitionistic fuzzy C-means clustering algorithm incorporating local information for brain image segmentation [J ] . Applied Soft Computing , 2016 , 46 : 543 - 557 .

YU B , ZHENG Z J , CAI M J , et al . FRCM: A fuzzy rough C-means clustering method [J ] . Fuzzy Sets and Systems , 2024 , 480 : 108860 .

ZENG W Y , MA R , YIN Q , et al . Hesitant fuzzy C-means algorithm and its application in image segmentation [J ] . Journal of Intelligent & Fuzzy Systems , 2020 , 39 ( 3 ): 3681 - 3695 .

PAL N R , PAL K , KELLER J M , et al . A possibilistic fuzzy C-means clustering algorithm [J ] . IEEE Transactions on Fuzzy Systems , 2005 , 13 ( 4 ): 517 - 530 .

KRISHNAPURAM R , KELLER J M . A possibilistic approach to clustering [J ] . IEEE Transactions on Fuzzy Systems , 1993 , 1 ( 2 ): 98 - 110 .

WANG Q S , WANG X P , FANG C , et al . Robust fuzzy C-means clustering algorithm with adaptive spatial & intensity constraint and membership linking for noise image segmentation [J ] . Applied Soft Computing , 2020 , 92 : 106318 .

KWAN R K , EVANS A C , PIKE G B . MRI simulation-based evaluation of image-processing and classification methods [J ] . IEEE Transactions on Medical Imaging , 1999 , 18 ( 11 ): 1085 - 1097 .

ARBELÁEZ P , MAIRE M , FOWLKES C , et al . Contour detection and hierarchical image segmentation [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2011 , 33 ( 5 ): 898 - 916 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

SwinT-Unet：基于双通道自注意力机制的超声图像分割方法

基于比尔朗伯定律的变分水平集模型

基于适应度指导交配限制策略的重组算子与多目标优化研究

ED-NAS: 基于神经网络架构搜索的陶瓷晶粒SEM图像分割方法