• DocumentCode
    1615659
  • Title

    Automated Vertebra Detection and Segmentation from the Whole Spine MR Images

  • Author

    Peng, Zhigang ; Zhong, Jia ; Wee, William ; Lee, Jing-Huei

  • Author_Institution
    Electr. & Comput. Eng. & Comput. Sci., Cincinnati Univ., OH
  • fYear
    2006
  • Firstpage
    2527
  • Lastpage
    2530
  • Abstract
    Our algorithm contains two major steps: the intervertebral disk localization step, and the vertebra detection and segmentation step. In the first step, we apply a model-based searching method to approximately locate all the intervertebral disk clues between adjacent vertebrae of the whole spine and the best slice selection. A new approach using an intensity profile on a polynomial function for fitting all these disk clues on the best slice is then used to refine the disk search process. Vertebra centers are detected, and initial boundaries are extracted in the second step. The initial test of the algorithm on the five sets of 7 sagittal slices locates all 23 intervertebral disk centers for the best slice of all five sets. For the evaluation of the boundary extraction of 22 vertebrae, our algorithm successfully locates 100%, 96.6%, 93.2%, 95.5%, 87.5% vertebra corners in image set No.1, 2, 3, 4, and 5, respectively
  • Keywords
    biomedical MRI; bone; image segmentation; medical image processing; automated vertebra detection; boundary extraction; intensity profile; intervertebral disk localization step; model-based searching method; polynomial function; vertebra segmentation; whole spine MR images; Bone diseases; Degenerative diseases; Image edge detection; Image segmentation; Medical treatment; Osteoporosis; Polynomials; Spine; Surgery; Testing; Vertebral detection; edge detection; intensity profile; vertebrae segmentation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the
  • Conference_Location
    Shanghai
  • Print_ISBN
    0-7803-8741-4
  • Type

    conf

  • DOI
    10.1109/IEMBS.2005.1616983
  • Filename
    1616983