• DocumentCode
    2248761
  • Title

    The rapid elicitation of knowledge about images using fuzzy information granules

  • Author

    Rossiter, Jonathan M.

  • Author_Institution
    RIKEN, Inst. of Phys. & Chem. Res., Nagoya, Japan
  • Volume
    2
  • fYear
    2004
  • fDate
    25-29 July 2004
  • Firstpage
    1159
  • Abstract
    We present a new method for tagging image regions using uncertain information granules. This tagging forms an efficient route for the elicitation of knowledge from domain experts with respect to images. We then use this uncertain granular information to train a fuzzy machine learner and then to classify unseen images. This method is particularly suited to applications where an expert input into the classification process is essential but where the expert´s time is in extremely short supply. Results are presented within the example domain of detecting the lung disease from computed tomography scans.
  • Keywords
    computerised tomography; diseases; fuzzy set theory; image classification; knowledge acquisition; learning (artificial intelligence); lung; medical image processing; computed tomography scans; fuzzy information granules; fuzzy machine learner; image classification; image knowledge elicitation; image region tagging; lung disease detection; uncertain granular information; Computed tomography; Fuzzy control; Image databases; Image segmentation; Labeling; Machine learning; Predictive models; Shape; Tagging; X-ray imaging;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems, 2004. Proceedings. 2004 IEEE International Conference on
  • ISSN
    1098-7584
  • Print_ISBN
    0-7803-8353-2
  • Type

    conf

  • DOI
    10.1109/FUZZY.2004.1375575
  • Filename
    1375575