Classifying torso deformity in scoliosis using orthogonal maps of the torso.
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.
Ajemba
Peter
P
Electrical and Computer Engineering, University of Alberta, W4-040 ECERF, and Rehabilitation Technology, Glenrose Rehabilitation Hospital, Edmonton, AB, Canada, T6G 2V4. ajemba@ece.ualberta.ca
Durdle
Nelson
N
Hill
Doug
D
Raso
James
J
D000293:Adolescent; D000328:Adult; D019412:Anatomy, Cross-Sectional; D001415:Back / Q000473:pathology; D002648:Child; D003198:Computer Simulation; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008297:Male; D008433:Mathematics; D010781:Photography; D012600:Scoliosis / Q000473:pathology*; D012720:Severity of Illness Index