• DocumentCode
    2354804
  • Title

    Automatic Image Segmentation Algorithm Based on PCNN and Fuzzy Mutual Information

  • Author

    Xiao, Zhiheng ; Shi, Jun ; Chang, Qiang

  • Author_Institution
    Sch. of Commun. & Inf. Eng., Shanghai Univ., Shanghai, China
  • Volume
    1
  • fYear
    2009
  • fDate
    11-14 Oct. 2009
  • Firstpage
    241
  • Lastpage
    245
  • Abstract
    The pulse coupled neural network (PCNN) algorithm has been effectively used in image segmentation. In this paper, we proposed a new image auto-segmentation algorithm based on PCNN and fuzzy mutual information (FMI). The image was firstly segmented by PCNN, and then FMI was used as the optimization criterion to automatically stop the segmentation with the optimal result. Different images were segmented by max-FMI PCNN, Otsu segmentation algorithm and max-entropy PCNN to evaluate the segmentation accuracy. The experimental results demonstrated that the CT and ultrasound images could be well segmented by the proposed algorithm with strong robustness against noise. The results suggest that the proposed algorithm can be used for medical image segmentation.
  • Keywords
    image segmentation; neural nets; optimisation; Otsu segmentation algorithm; automatic image segmentation algorithm; fuzzy mutual information; image auto-segmentation algorithm; max-FMI PCNN; max-entropy PCNN; medical image segmentation; optimization criterion; pulse coupled neural network algorithm; Active contours; Biomedical imaging; Deformable models; Image segmentation; Joining processes; Mutual information; Neural networks; Neurons; Pulse generation; Pulse modulation; PCNN; fuzzy mutual information; image segmentation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer and Information Technology, 2009. CIT '09. Ninth IEEE International Conference on
  • Conference_Location
    Xiamen
  • Print_ISBN
    978-0-7695-3836-5
  • Type

    conf

  • DOI
    10.1109/CIT.2009.92
  • Filename
    5329529