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.
Conference Paper: Automatic Spinal Deformity Detection Based on Neural Network.[Show abstract] [Hide abstract]
ABSTRACT: We propose a technique for automatic spinal deformity detection method from moiré topographic images. Normally the moiré stripes show a symmetric pattern, 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. Displacement of local centroids is evaluated statistically between the left-hand side and the right-hand side regions of the moiré images with respect to the extracted middle line. The degree of the displacement learned by a neural network employing the back propagation algorithm. An experiment was performed employing 1,200 real moiré images (600 normal and 600 abnormal) and 89% of the images were classified correctly by the NN.Medical Image Computing and Computer-Assisted Intervention - MICCAI 2003, 6th International Conference, Montréal, Canada, November 15-18, 2003, Proceedings, Part I; 01/2003
Conference Paper: Spinal Deformity Detection Employing Back Propagation on Neural Network.[Show abstract] [Hide abstract]
ABSTRACT: We propose a new technique for automatic spinal deformity detection from moire topographic images. Normally the moire stripes of a human body show a symmetric pattern. 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. Displacement of local centroids and difference of gray value are calculated between the left-hand side and the right-hand side regions of the moire images with respect to the extracted middle line. Extracted 4 feature vectors (mean value and standard deviation from the each displacement) from the left-hand side and right-hand side rectangle areas apply to train a neural network. An experiment was performed employing 1,200 real moire images and 90.3% of the images were classified correctly.Pattern Recognition and Image Analysis, Third International Conference on Advances in Pattern Recognition, ICAPR 2005, Bath, UK, August 22-25, 2005, Proceedings, Part II; 01/2005
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ABSTRACT: This paper presents a technique for automating human scoliosis detection by computer based on moiré topographic images of human backs. Scoliosis is a serious disease often suffered by teenagers. For prevention, screening is performed at schools in Japan employing a moiré method in which doctors inspect moiré images of subjects' backs visually. The inspection of a large number of moiré 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 moiré images in the present technique. The technique was applied to real moiré 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 moiré images and classified 80 images in the remaining two sets. The technique finally achieved the average classification rate of 88.3%.IEEE Transactions on Medical Imaging 01/2002; 20(12):1314-20. · 4.03 Impact Factor