• DocumentCode
    2505474
  • Title

    Automated segmentation of recuts abdominis muscle using shape model in X-ray CT images

  • Author

    Kamiya, N. ; Zhou, X. ; Chen, H. ; Muramatsu, C. ; Hara, T. ; Yokoyama, R. ; Kanematsu, M. ; Hoshi, H. ; Fujita, H.

  • Author_Institution
    Dept. of Intell. Image Inf., Gifu Univ., Gifu, Japan
  • fYear
    2011
  • fDate
    Aug. 30 2011-Sept. 3 2011
  • Firstpage
    7993
  • Lastpage
    7996
  • Abstract
    Our purpose in this study is to segment the rectus abdominis muscle region in X-ray CT images, and we propose a novel recognition method based on the shape model. In this method, three steps are included in the segmentation process. The first is to generate a shape model for the rectus abdominis muscle. The second is to recognize anatomical feature points corresponding to the origin and insertion of the muscle, and the third is to segment the rectus abdominis muscles based on the shape model. We generated the shape model from 20 CT cases and tested the model to recognize the muscle in 20 other CT cases. The average values for the Jaccard similarity coefficient (JSC) and true segmentation coefficient (TSC) were 0.841 and 0.863, respectively. The results suggest the validity of the model-based segmentation for the rectus abdominis muscle.
  • Keywords
    computerised tomography; image segmentation; medical image processing; muscle; Jaccard similarity coefficient; X-ray CT images; anatomical feature points; automated segmentation; rectus abdominis muscle; shape model; true segmentation coefficient; Computed tomography; Image recognition; Image segmentation; Muscles; Shape; Solid modeling; Torso; Automation; Humans; Imaging, Three-Dimensional; Models, Anatomic; Radiographic Image Interpretation, Computer-Assisted; Rectus Abdominis; Tomography, X-Ray Computed;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE
  • Conference_Location
    Boston, MA
  • ISSN
    1557-170X
  • Print_ISBN
    978-1-4244-4121-1
  • Electronic_ISBN
    1557-170X
  • Type

    conf

  • DOI
    10.1109/IEMBS.2011.6091971
  • Filename
    6091971