Conference Paper

A Method of Analyzing a Shape with Potential Symmetry and Its Application to Detecting Spinal Deformity.

DOI: 10.1007/BFb0034987 Conference: Computer Vision, Virtual Reality and Robotics in Medicine, First International Conference, CVRMed'95, Nice, France, April 3-6, 1995, Proceedings
Source: DBLP


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.

1 Read
  • Source
    • "The middle line is defined in the first place on I(x, y). Since the moire pattern of a human back usually exhibits asymmetry, a potential symmetry axis[4] is extracted from I(x, y) and the axis is regarded as the middle line of the back. Let the middle line be located at x=m. "
    [Show abstract] [Hide abstract]
    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%.
    Preview · Conference Paper · Jan 1998
  • Source
    [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.
    Preview · Conference Paper · Jan 1996
  • [Show abstract] [Hide abstract]
    ABSTRACT: Spinal deformities, a condition in which the spine deviates to the left or right from the midline, are on the rise, particularly among elementary and middle-school students. Group diagnoses are being performed at schools for the purpose of early detection, but the burden on physicians is high due to the need to process many images. Automated diagnosis using computers is highly desirable from the standpoint of treatment. In this paper the authors establish a left–right rectangular processing region in a moiré image of a human back, then propose a method to automatically detect spinal deformities with the differences in the centroid positions as a feature. Using an approximately symmetrical reference axis which closely follows the midline of a person, and the amount of the feature from the centroid position of the left–right rectangular region as centered on the reference axis, the authors' method discriminates between normal cases and abnormal cases using a linear discrimination function on a two-dimensional surface. If this kind of primary diagnosis of spinal deformities can be automated, group diagnosis in schools would be more efficient and useful. The authors use their method, perform discrimination tests on 120 normal and abnormal cases, and find a recognition rate of 87.9%. © 2001 Scripta Technica, Syst Comp Jpn, 32(7): 20–28, 2001
    No preview · Article · Jun 2001 · Systems and Computers in Japan
Show more