Automatic classification of spinal deformity by using four symmetrical features on the moire images

Spinal deformity is a disease mainly suffered by teenagers during their growth stage particularly from element school to middle school. There are many different causes of abnormal spinal curves, but all of them are unknown. The most common type is termed “idiopathic” that show 80% of the spinal deformity. Spinal deformity is a serious disease, mainly suffered by teenagers, especially girl’s student, during their growth stage.To find the spinal deformity in early stage, orthopedists have traditionally performed on children a painless examination called a forward bending test in mass screening of school. But this test is neither objective nor reproductive, and the inspection takes much time when applied to medical examination in schools. To solve this problem, a moire method has been proposed which takes moire topographic images of human subject backs and checks symmetry/asymmetry of the moire patterns in a two-dimensional way. In this paper, we propose a method for automatic classification of spinal deformity from moire topographic images by extracting four symmetrical features of the left-hand and right-hand side on the moire image. Feature of asymmetry degrees are applied to train employing the classifier such as Artificial Neural Network, Support Vector Machine, Self-Organization Map and AdaBoost.