Quantification of localized vertebral deformities using a sparse wavelet-based shape model.
Medical experts often examine hundreds of spine x-ray images to determine existence of various pathologies. Common pathologies of interest are anterior osteophites, disc space narrowing, and wedging. By careful inspection of the outline shapes of the vertebral bodies, experts are able to identify and assess vertebral abnormalities with respect to the pathology under investigation. In this paper, we present a novel method for quantification of vertebral deformation using a sparse shape model. Using wavelets and Independent component analysis (ICA), we construct a sparse shape model that benefits from the approximation power of wavelets and the capability of ICA to capture higher order statistics in wavelet space. The new model is able to capture localized pathology-related shape deformations, hence it allows for quantification of vertebral shape variations. We investigate the capability of the model to predict localized pathology related deformations. Next, using support-vector machines, we demonstrate the diagnostic capabilities of the method through the discrimination of anterior osteophites in lumbar vertebrae. Experiments were conducted using a set of 150 contours from digital x-ray images of lumbar spine. Each vertebra is labeled as normal or abnormal. Results reported in this work focus on anterior osteophites as the pathology of interest.
Zewail
R
R
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada.
Elsafi
A
A
Durdle
N
N
D015415:Biomarkers; D001696:Biomechanical Phenomena; D006801:Humans; D008159:Lumbar Vertebrae / Q000473:pathology*; D015233:Models, Statistical; D008962:Models, Theoretical; D012600:Scoliosis / Q000473:pathology*; D013121:Spinal Curvatures / Q000473:pathology*
D015415:Biomarkers