• DocumentCode
    2090686
  • Title

    A Novel Modified Kernel Fuzzy C-Means Clustering Algorithm on Image Segementation

  • Author

    Yu, Chun-yan ; Li, Ying ; Liu, Ai-lian ; Liu, Jing-hong

  • Author_Institution
    Inst. of Inf. & Sci. Technol., Dalian Maritime Univ., Dalian, China
  • fYear
    2011
  • fDate
    24-26 Aug. 2011
  • Firstpage
    621
  • Lastpage
    626
  • Abstract
    Image segmentation plays an important role in imaging analysis. Based on the Mercer kernel, the fuzzy kernel c-means clustering algorithm (FKCM) is derived from the fuzzy c-means clustering algorithm (FCM).The FKCM algorithm that provides image clustering can improve accuracy significantly compared with classical fuzzy c-Means algorithms. In this paper, considering the advantages of KFCM, we propose a novel modified kernel fuzzy c means(NMKFCM) algorithm based on conventional KFCM which incorporates the neighbor term into its objective function. The results of experiments performed on synthetic and real medical images show that the new algorithm is effective and efficient, and has better performance in noisy images.
  • Keywords
    fuzzy reasoning; fuzzy set theory; image segmentation; medical image processing; pattern clustering; FKCM algorithm; Mercer kernel; image clustering; image segmentation; imaging analysis; modified kernel fuzzy c-means clustering algorithm; noisy image; objective function; real medical image; synthetic medical image; Accuracy; Algorithm design and analysis; Biomedical imaging; Clustering algorithms; Image segmentation; Kernel; Noise;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Science and Engineering (CSE), 2011 IEEE 14th International Conference on
  • Conference_Location
    Dalian, Liaoning
  • Print_ISBN
    978-1-4577-0974-6
  • Type

    conf

  • DOI
    10.1109/CSE.2011.109
  • Filename
    6062941