• DocumentCode
    2376076
  • Title

    Automated recognition of the psoas major muscles on X-ray CT images

  • Author

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

  • Author_Institution
    Dept. of Intell. Image Inf., Gifu Univ., Gifu, Japan
  • fYear
    2009
  • fDate
    3-6 Sept. 2009
  • Firstpage
    3557
  • Lastpage
    3560
  • Abstract
    The purpose of this study is to recognize the psoas major muscle on X-ray CT images. For this purpose, we propose a novel recognition method. The recognition process in this method involves three steps: the generation of a shape model for the psoas major muscle, recognition of anatomical points such as the origin and insertion, and the recognition of the psoas major muscles by the use of the shape model. We generated the shape model using 20 CT cases and tested the model for recognition in 20 other CT cases. The average Jaccard similarity coefficient (JSC) and reproducibility rate were 0.704 and 0.783, respectively. Experimental results indicate that our method was effective for a 2-D cross-sectional area (CSA) analysis.
  • Keywords
    computerised tomography; diagnostic radiography; image recognition; medical image processing; muscle; shape recognition; 2D cross-sectional area analysis; Jaccard similarity coefficient; X-ray CT images; automated recognition; psoas major muscles; reproducibility rate; shape model generation; Algorithms; Artificial Intelligence; Bone and Bones; Diagnosis, Computer-Assisted; Diagnostic Imaging; Female; Humans; Imaging, Three-Dimensional; Male; Pattern Recognition, Automated; Psoas Muscles; Radiographic Image Interpretation, Computer-Assisted; Reproducibility of Results; Tomography, X-Ray Computed; X-Rays;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE
  • Conference_Location
    Minneapolis, MN
  • ISSN
    1557-170X
  • Print_ISBN
    978-1-4244-3296-7
  • Electronic_ISBN
    1557-170X
  • Type

    conf

  • DOI
    10.1109/IEMBS.2009.5332597
  • Filename
    5332597