Discrimination of Spinal Deformity Employing Discriminant Analysis on the $Moir\acute{e}$ Images

In this paper, we propose a technique for automatic spinal deformity detection from $moir\acute{e}$ topographic images. Normally the $moir\acute{e}$ stripes show symmetry as a human body is almost symmetric. According to the progress of the deformity of a spine, asymmetry becomes larger. Numerical representation of the degree of asymmetry is therefore useful in evaluating the deformity. First, displacement of local centroids and difference of gray values are evaluated statistically between the left- and the right-hand side regions of the $moir\acute{e}$ images with respect to the extracted middle line. We classify the moire images into two categories i.e., normal and abnormal cases from the features, employing discriminant analysis. An experiment was performed employing 1,200 $moir\acute{e}$ images and 85% of the images were classified correctly.