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Classifying torso deformity in scoliosis using orthogonal maps of the torso

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Abstract

Analysis of three-dimensional (3D) images of human torsos for torso deformities such as scoliosis requires classifying torso distortion. Assessing torso distortion from 3D images is not trivial as actual torsos are non-symmetric and show an outstanding range of variations leading to high classification errors. As the degree of spinal deformity (and classification of torso shape) influences scoliosis treatment options, the development of more accurate classification procedures is desirable. This paper presents a technique for assessing torso shape and classifying scoliosis into mild, moderate and severe categories using two indices, ‘twist’ and ‘bend’, obtained from orthogonally transformed images of the complete torso surface called orthogonal maps. Four transforms (axial line, unfolded cylinder, enclosing cylinder and subtracting cylinder) were used. Blind tests on 361 computer models with known deformation parameter values show 100% classification accuracy. Tests on eight volunteers without scoliosis validated the system and tests on 22 torso images of volunteers with scoliosis showed up to 95.5% classification accuracy. In addition to classifying scoliosis, orthogonal maps present the entire torso in one view and are viable for use in scoliosis clinics for monitoring the progression of scoliosis.

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Notes

  1. Other classifications systems such as that by Lenke et al. [14] are also used.

  2. INSPECK Inc., Montreal, QC, Canada

  3. KONICA-MINOLTA Photo Imaging Inc., Mahwah, NJ, USA

  4. The maximal diameter of a closed 2D shape such as a torso cross-section is the longest distance between any two points in the convex hull of the shape.

  5. The orthogonal maps of a perfect circular cylinder are devoid of contours.

  6. As twist and bend indices are basis vectors in deformity space, the classification was based on their norm.

  7. Their torso scans were used to check the null-response pattern of the system.

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Acknowledgments

The authors will like to thank Dr. Marc Moreau and Dr. James Mahood for their help in obtaining the full torso scans of scoliosis patients. P. O. Ajemba was supported by the Province of Alberta Informatics Centre for Research Excellence (iCORE), the Bone and Joint Health Training Program of the Canadian Institute for Health Research (CIHR) and the Alberta Ingenuity Scholarship.

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Authors and Affiliations

  1. Electrical and Computer Engineering, University of Alberta, W4-040 ECERF, Edmonton, AB, Canada, T6G 2V4

    Peter Ajemba & Nelson Durdle

  2. Rehabilitation Technology, Glenrose Rehabilitation Hospital, Edmonton, AB, Canada, T5G 0B7

    Doug Hill & James Raso

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  1. Peter Ajemba
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  2. Nelson Durdle
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  3. Doug Hill
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  4. James Raso
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Correspondence to Peter Ajemba.

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Ajemba, P., Durdle, N., Hill, D. et al. Classifying torso deformity in scoliosis using orthogonal maps of the torso. Med Bio Eng Comput 45, 575–584 (2007). https://doi.org/10.1007/s11517-007-0192-z

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  • DOI: https://doi.org/10.1007/s11517-007-0192-z

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