• DocumentCode
    3365348
  • Title

    Research of automatic medical image segmentation algorithm based on Tsallis entropy and improved PCNN

  • Author

    Weili, Shi ; Yu, Miao ; Zhanfang, Chen ; Hongbiao, Zhang

  • Author_Institution
    Changchun Univ. of Sci. & Technol., Changchun, China
  • fYear
    2009
  • fDate
    9-12 Aug. 2009
  • Firstpage
    1004
  • Lastpage
    1008
  • Abstract
    It needs set parameters on image segmentation based on PCNN (Pulse Coupled Neural Network) now. This paper points out the new method for medical image segmentation based on improved PCNN and Tsallis entropy. The new methods can automatically segment the medical images without selecting the PCNN parameters. It gets the best results with combining with the Tsallis entropy. The new method is very useful for PCNN application in the medical images segmentation.
  • Keywords
    entropy; image segmentation; medical image processing; neural nets; Tsallis entropy; automatic medical image segmentation algorithm; pulse coupled neural network; Biomedical imaging; Brain modeling; Cities and towns; Deformable models; Entropy; Equations; Histograms; Image segmentation; Mathematical model; Mathematics; Artificial Intelligence; Medical Image Segmentation; PCNN; Tsallis entropy;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Mechatronics and Automation, 2009. ICMA 2009. International Conference on
  • Conference_Location
    Changchun
  • Print_ISBN
    978-1-4244-2692-8
  • Electronic_ISBN
    978-1-4244-2693-5
  • Type

    conf

  • DOI
    10.1109/ICMA.2009.5246315
  • Filename
    5246315
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3365348