A Method of Analyzing a Shape with Potential Symmetry and Its Application to Detecting Spinal Deformity.
ABSTRACT This paper describes a technique for analyzing a shape with potential symmetry which includes approximate symmetry and original symmetry. A technique is proposed for identifying a symmetry axis of a shape with potential axial symmetry by searching for the largest symmetric subset of the shape. It is applied to the axial detection and asymmetry evaluation of Moire topographic images of human backs to automate spinal deformity inspection. Some experimental results are shown and discussion is given.
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ABSTRACT: A technique is described for classifying abnormal cases and normal cases in automatic spinal deformity analysis by computer based on moire topographic images of human backs. Displacement of local centroids is evaluated statistically between the left-hand side and the right-hand side of the moire images. The technique was applied to real subjects images in order to draw a distinction between 60 normal and 60 abnormal cases. According to the leave-out method, the entire data was separated into three sets. The linear discriminant function based on the Mahalanobis distance was defined on the 2-D feature space employing one of the data sets containing 40 moire images and classified 80 images in the remaining two sets. The average classification rate was 87.9%.Proceedings of IAPR Workshop on Machine Vision Applications, MVA 1998, November 17-19, 1998, Chiba, Japan; 01/1998
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ABSTRACT: Spinal deformity is a serious disease, mainly suffered by teenagers during their growth stage. In this paper, we propose a new technique for an automatic judgment method of spinal deformity from moire topographic images. Normally the moire stripes show a symmetric pattern, as a human subject is almost symmetric. According to the progress of the deformity of a spine, asymmetry becomes larger. Medical doctors check the asymmetric moire patter from the moire topographic image on visual screening. Nu-merical representation of the degree of asymmetry is therefore useful in evaluating the deformity. In this study, displacement of local centroids and difference of gray values are evaluated statistically between the left-hand side and the right-hand side regions of the moire images with respect to the extracted middle line. The degree of the displacement and differences of gray values are learned by using a neural network employing the back propagation algorithm and satisfactory classification rates are obtained. An experiment was performed employing 1200 real moire images and 90.3% of the images were classified correctly.
Conference Paper: Automatic Spinal Deformity Detection by Two Characteristic Axes.[Show abstract] [Hide abstract]
ABSTRACT: line of human and the other axis is the principal This paper proposes a technique for judging spinal deformity from a moire image of a human back. The middle line and the principal axes of the back are extracted from the moire image and their difference is numerically evaluated. For the extraction of the middle line, the potential symmetry analysis technique is employed, whereas the principal axes are obtained from the moment of inertia matrix defined on the moire image. Experimental results are given and some issues are discussed.Proceedings of IAPR Workshop on Machine Vision Applications, MVA 1996, November 12-14, 1996, Tokyo, Japan; 01/1996