• DocumentCode
    3108620
  • Title

    Medical image granulation by fuzzy inference

  • Author

    Hata, Yutaka ; Hirano, Shoji ; Kamiura, Naotake

  • Author_Institution
    Dept. of Comput. Eng., Himeji Inst. of Technol., Hyogo, Japan
  • fYear
    1998
  • fDate
    20-21 Aug 1998
  • Firstpage
    188
  • Lastpage
    192
  • Abstract
    The paper proposes a scheme of image granulation using the fuzzy inference technique. For a region of interest (ROI) in a medical image, the authors describe knowledge needed to granulate the ROI, for example, knowledge of intensity, location and so on. Generally, one cannot granulate the ROI without employing the whole of the knowledge. Fuzzy inference rules of the derived knowledge can accommodate the granulation. After the inference results are compiled to a total degree, resultant data is obtained. Clustering or a region growing technique is used to granulate the ROI. The experimental results on human brain MR images and human foot CT images show that the method can precisely granulate the ROI
  • Keywords
    fuzzy logic; image segmentation; inference mechanisms; medical image processing; clustering; fuzzy inference; human brain MR images; human foot CT images; knowledge; medical image granulation; region growing technique; region of interest; Biomedical engineering; Biomedical imaging; Computed tomography; Data visualization; Foot; Fuzzy logic; Gravity; Humans; Image segmentation; Pixel;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Information Processing Society - NAFIPS, 1998 Conference of the North American
  • Conference_Location
    Pensacola Beach, FL
  • Print_ISBN
    0-7803-4453-7
  • Type

    conf

  • DOI
    10.1109/NAFIPS.1998.715562
  • Filename
    715562