• DocumentCode
    749617
  • Title

    Characterizing Torso Shape Deformity in Scoliosis Using Structured Splines Models

  • Author

    Ajemba, Peter O. ; Durdle, Nelson G. ; Raso, V. James

  • Author_Institution
    Electr. & Comput. Eng. Dept., Univ. of Alberta, Edmonton, AB
  • Volume
    56
  • Issue
    6
  • fYear
    2009
  • fDate
    6/1/2009 12:00:00 AM
  • Firstpage
    1652
  • Lastpage
    1662
  • Abstract
    This paper describes a method of characterizing the torso shape deformity associated with scoliosis by both its type and severity. This problem is challenging because regular human torsos show an astounding range of variations that is only compounded by scoliosis, and it is difficult to isolate natural shape variations from those caused by scoliosis. Torso shape characterization is important in the clinical management of scoliosis because torso aesthetics is a key concern that influences a patient´s quality of life. Our method involves modeling 3-D torso range images into structured sequences of 3-D spline curves stacked along the spine. We obtain local shape measures from points of maximal curvature (dominant points) along each torso cross section by evaluating the relative symmetry of the spline curve at that cross section. This results in a scalable characterization scheme for torso deformity type and a measure of torso deformity severity. We assess the accuracy and precision of this shape characterization scheme, and its relationship to the actual deformities present in the underlying spine.
  • Keywords
    biomechanics; bone; deformation; image sequences; medical disorders; medical image processing; orthopaedics; splines (mathematics); 3D spline curve; 3D torso range image modelling; scoliosis; shape analysis; structured image sequence; structured splines model; torso shape deformity; Back; Computed tomography; Deformable models; Diagnostic radiography; Humans; Magnetic resonance imaging; Object recognition; Patient monitoring; Quality management; Shape measurement; Spine; Torso; Dominant points; object recognition; scoliosis; shape analysis; splines; Adolescent; Algorithms; Back; Child; Female; Humans; Image Processing, Computer-Assisted; Male; Models, Anatomic; Predictive Value of Tests; Scoliosis; Spine; Young Adult;
  • fLanguage
    English
  • Journal_Title
    Biomedical Engineering, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9294
  • Type

    jour

  • DOI
    10.1109/TBME.2009.2020333
  • Filename
    4838958