AUTOMATIC DETECTION OF SPINAL DEFORMITY BASED ON STATISTICAL FEATURES FROM THE MOIRE TOPOGRAPHIC IMAGES

Abstract

Spinal deformity is one of 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. 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 backs and checks symmetry/asymmetry of their moire patterns. In this paper, we propose a method for automatic judgment of spinal deformity which is obtained moire topographic images based on statistical features on the moire image. Statistical feature of asymmetry degrees are applied to train employing the classifier such as Artificial Neural Network, Support Vector Machine, Self-Organization Map and AdaBoost.