• DocumentCode
    1928706
  • Title

    A ROI-Based Mining Method with Medical Domain Knowledge Guidance

  • Author

    Pan, Haiwei ; Han, Qilong ; Yin, Guisheng ; Zhang, Wei ; Li, Jianzhong ; Ni, Jun

  • Author_Institution
    Coll. of Comput. Sci. & Technol., Harbin Eng. Univ., Harbin
  • fYear
    2008
  • fDate
    28-29 Jan. 2008
  • Firstpage
    91
  • Lastpage
    97
  • Abstract
    Image mining is a growing research focus and is more than just an extension of data mining to image domain but an interdisciplinary endeavor. Very few people have systematically investigated this field. Mining association rules in medical images is an important part in domain- specific application image mining because there are several technical aspects which make this problem challenging. In this paper, we firstly incorporate the domain knowledge into the ROI extraction algorithm and ROI clustering algorithm, then we extend the concept of association rule based on ROI and image in medical images, and propose two algorithms to discover frequent item-sets and mine interesting association rules from medical images. Some interesting results are obtained by our program and we believe many of the problems we come across are likely to appear in other domains.
  • Keywords
    data mining; image retrieval; medical image processing; Image mining; ROI clustering algorithm; ROI extraction algorithm; ROI-based mining method; data mining; domain- specific application; medical domain knowledge guidance; medical images; Association rules; Biomedical engineering; Biomedical imaging; Clustering algorithms; Data engineering; Data mining; Focusing; Image databases; Relational databases; Satellites; association rule; domain knowledge; medical image mining;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Internet Computing in Science and Engineering, 2008. ICICSE '08. International Conference on
  • Conference_Location
    Harbin
  • Print_ISBN
    978-0-7695-3112-0
  • Electronic_ISBN
    978-0-7695-3112-0
  • Type

    conf

  • DOI
    10.1109/ICICSE.2008.91
  • Filename
    4548240