• DocumentCode
    1949201
  • Title

    A Novel Automatic Framework for Scoliosis X-Ray Image Retrieval

  • Author

    XU, Zhiping ; Pan, Jinhong ; Zhang, Shiyong

  • Author_Institution
    Fudan Univ., Shanghai
  • fYear
    2007
  • fDate
    12-17 Aug. 2007
  • Firstpage
    2482
  • Lastpage
    2485
  • Abstract
    The paper proposed a novel automatic scoliosis X-ray image retrieval framework based on the global statistical feature of edge, edge co-occurrence matrix (ECM) and the local geometrical feature set of the whole spine, angle of each spine curve. The ECM is based on the statistical feature attained from the edge detection operators which applied on the image. The eigenvectors obtained from principle component analysis (PCA) of the ECM can preserve the high spatial frequencies components, so they are well suited for shape as well as texture representation. The geometrical feature like the Cobb´s angle of each spine curve could be derived from the image segmentation based on the Intersecting Cortical Model, which is elicitation of the Eckhorn´s model. The experiment shows that the framework shows good accuracy for the input query X-ray image in our work.
  • Keywords
    X-ray imaging; edge detection; eigenvalues and eigenfunctions; feature extraction; image representation; image retrieval; image segmentation; image texture; matrix algebra; medical image processing; principal component analysis; Intersecting Cortical Model; edge cooccurrence matrix; edge detection; eigenvectors; global statistical feature; image segmentation; local geometrical feature set; principle component analysis; scoliosis; spine curve; texture representation; x-ray image retrieval; Electrochemical machining; Frequency; Image edge detection; Image retrieval; Image segmentation; Image texture analysis; Principal component analysis; Shape; Solid modeling; X-ray imaging;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2007. IJCNN 2007. International Joint Conference on
  • Conference_Location
    Orlando, FL
  • ISSN
    1098-7576
  • Print_ISBN
    978-1-4244-1379-9
  • Electronic_ISBN
    1098-7576
  • Type

    conf

  • DOI
    10.1109/IJCNN.2007.4371348
  • Filename
    4371348