KEYWORDS: 3D image processing, Classification systems, X-rays, 3D acquisition, Image classification, Spine, Principal component analysis, X-ray imaging, 3D modeling, Computing systems
Adolescent idiopathic scoliosis (AIS) is a deformity of the spine manifested by asymmetry and deformities of the external
surface of the trunk. Classification of scoliosis deformities according to curve type is used to plan management of scoliosis
patients. Currently, scoliosis curve type is determined based on X-ray exam. However, cumulative exposure to X-rays
radiation significantly increases the risk for certain cancer. In this paper, we propose a robust system that can classify
the scoliosis curve type from non invasive acquisition of 3D trunk surface of the patients. The 3D image of the trunk is
divided into patches and local geometric descriptors characterizing the surface of the back are computed from each patch
and forming the features. We perform the reduction of the dimensionality by using Principal Component Analysis and
53 components were retained. In this work a multi-class classifier is built with Least-squares support vector machine
(LS-SVM) which is a kernel classifier. For this study, a new kernel was designed in order to achieve a robust classifier
in comparison with polynomial and Gaussian kernel. The proposed system was validated using data of 103 patients with
different scoliosis curve types diagnosed and classified by an orthopedic surgeon from the X-ray images. The average rate
of successful classification was 93.3% with a better rate of prediction for the major thoracic and lumbar/thoracolumbar
types.
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