Automatic scoliosis detection based on local centroids evaluation on moire topographic images of human backs

This paper presents a technique for automating human scoliosis detection by computer based on moire topographic images of human backs. Scollosis is a serious disease often suffered by teenagers. For prevention, screening is performed at schools in Japan employing a moire method in which doctors inspect moire images of subjects' backs visually. The inspection of a large number of moire images collected by the school screening causes exhaustion of doctors and leads to misjudgment. Computer-aided diagnosis of scoliosis has, therefore, been requested eagerly by orthopedists. To automate the inspection process, unlike existent three-dimensional techniques, displacement of local centroids is evaluated two-dimensionally between the left-hand side and the right-hand side of the moire images in the present technique. The technique was applied to real moire images to draw a distinction between normal and abnormal cases. According to the leave-out method, the entire 120 image data (60 normal and 60 abnormal) were separated into three data sets. The linear discriminant function based on Mahalanobis distance was defined on the two-dimensional feature space employing one of the data sets containing 40 moire images and classified 80 images in the remaining two sets. The technique finally achieved the average classification rate of 88.3%.