2007 Fiscal Year Final Research Report Summary
Development of Spinal Deformity Detection System by Use of Moire Images
| Project/Area Number |
18560414
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Measurement engineering
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| Research Institution | Kyushu Institute of Technology |
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Principal Investigator |
KIM Hyoungseop Kyushu Institute of Technology, Faculty of Engineering, Associate Professor (80295005)
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| Project Period (FY) |
2006 – 2007
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| Keywords | mass screening / spinal deformity / moire image / computer aided diagnosis |
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| Research Abstract |
In order to check the presence of spinal deformity in the early stage, orthopedists have traditionally performed on children a painless examination called a forward-bending test in school screening. In forward-bending test, mainly medical doctor checks to see if one shoulder is lower than the other. But this test is neither reproductive nor objective. Moreover, the inspection takes much time when applied to medical examination in schools. To overcome these difficulties, a moire method has been proposed which takes moire topographic images of human subject backs and checks symmetry/asymmetry of the moire patterns in a two-dimensional way on visual screening. In this paper, we propose a new technique for automatic detection of spinal deformity from moire topographic images.
In this study, we propose new techniques for automatic detection of spinal deformity from moire topographic images. In the first stage, once the original moire image is fed into computer, the middle line of the subject's back is extracted on the moire image by employing the approximate symmetry analysis. Region of interest are then automatically selected on the moire image from its upper part to the lower part. Numerical representation of the degree of asymmetry is therefore useful in evaluating the deformity. Displacement of local centroids and difference of gray value from the density feature and angle of shoulders from the shape index 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 statistical feature vectors from the left-hand side and right-hand side rectangle areas apply to train the NN and SVMs. In the experimental, satisfactory classification results are achieved.
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