IMAGE SEGMENTATION BY A ROBUST GENERALIZED FUZZY C-MEANS ALGORITHM

被引:0
|
作者
Zhang, Hui [1 ,2 ]
Wu, Q. M. Jonathan [1 ]
Thanh Minh Nguyen [1 ]
机构
[1] Univ Windsor, Dept Elect & Comp Engn, Windsor, ON N9B 3P4, Canada
[2] Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing, Jiangsu, Peoples R China
来源
2013 20TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2013) | 2013年
关键词
Fuzzy C-Means; Generalized Mean; Image segmentation; Spatial constraints; MODELS;
D O I
暂无
中图分类号
TB8 [摄影技术];
学科分类号
0804 ;
摘要
Fuzzy c-means (FCM) has been considered as an effective algorithm for image segmentation. However, it lacks of sufficient robustness to image noise. In this paper, we propose a simple and effective method to make the traditional FCM more robust to noise, with the help of generalized mean. Traditional FCM can be considered as a linear combination of membership and distance (function) from the expression of its mathematical formula. The proposed generalized FCM (GFCM) is generated by applying generalized mean on these two items. We impose generalized mean on membership to incorporate local spatial information and cluster information, and on distance function to incorporate local spatial information and observation information (image intensity value). Thus, our GFCM is more robust to image noise with the spatial constraints: the generalized mean. The performance of our proposed algorithm, compared with state-of-the-art technologies including modified FCM, HMRF and their hybrid models, demonstrates its improved robustness and effectiveness.
引用
收藏
页码:4024 / 4028
页数:5
相关论文
共 50 条
  • [1] Image segmentation by generalized hierarchical fuzzy C-means algorithm
    Zheng, Yuhui
    Jeon, Byeungwoo
    Xu, Danhua
    Wu, Q. M. Jonathan
    Zhang, Hui
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2015, 28 (02) : 961 - 973
  • [2] An automatic fuzzy c-means algorithm for image segmentation
    Li, Yan-ling
    Shen, Yi
    SOFT COMPUTING, 2010, 14 (02) : 123 - 128
  • [3] Robust spatially constrained fuzzy c-means algorithm for brain MR image segmentation
    Ji, Zexuan
    Liu, Jinyao
    Cao, Guo
    Sun, Quansen
    Chen, Qiang
    PATTERN RECOGNITION, 2014, 47 (07) : 2454 - 2466
  • [4] Spatial α-Trimmed Fuzzy C-Means Algorithm to Image Segmentation
    Vela-Rincon, Virna V.
    Mujica-Vargas, Dante
    Mejia Lavalle, Manuel
    Magadan Salazar, Andrea
    PATTERN RECOGNITION (MCPR 2020), 2020, 12088 : 118 - 128
  • [5] An Image Segmentation Algorithm Based On Fuzzy C-Means Clustering
    Zhang Xinbo
    Jiang Li
    PROCEEDINGS OF 2009 CONFERENCE ON COMMUNICATION FACULTY, 2009, : 123 - 126
  • [6] Generalized rough fuzzy c-means algorithm for brain MR image segmentation
    Ji, Zexuan
    Sun, Quansen
    Xia, Yong
    Chen, Qiang
    Xia, Deshen
    Feng, Dagan
    COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2012, 108 (02) : 644 - 655
  • [7] An Image Segmentation Algorithm Based on Fuzzy C-Means Clustering
    Zhang, Xin-bo
    Jiang, Li
    ICDIP 2009: INTERNATIONAL CONFERENCE ON DIGITAL IMAGE PROCESSING, PROCEEDINGS, 2009, : 22 - 26
  • [8] Robust Intuitionistic Fuzzy c-means Clustering Algorithm for Brain Image Segmentation
    Monalisa, Achalla
    Swathi, Dasari
    Karuna, Yepuganti
    Saladi, Saritha
    PROCEEDINGS OF THE 2018 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATION AND SIGNAL PROCESSING (ICCSP), 2018, : 781 - 785
  • [9] An automatic fuzzy c-means algorithm for image segmentation
    Yan-ling Li
    Yi Shen
    Soft Computing, 2010, 14 : 123 - 128
  • [10] Pythagorean fuzzy C-means algorithm for image segmentation
    Ma, Rong
    Zeng, Wenyi
    Song, Guangcheng
    Yin, Qian
    Xu, Zeshui
    INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2021, 36 (03) : 1223 - 1243