Automatic Scoliosis Detection Based on Discriminant Analysis

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  • 判別分析によるモアレ画像からの脊柱側彎症自動識別
  • ハンベツ ブンセキ ニ ヨル モアレ ガゾウ カラ ノ セキチュウ ソクワンショウ ジドウ シキベツ

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Abstract

In this paper, we propose a technique for automatic scoliosis detection from moire topographic images. Normally the moire stripes show symmetry 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. First, displacement of local centroids and difference of gray values are evaluated statistically between the left- and the right-hand side regions of the moire images with respect to the extracted middle line. We classify the moire images into two categories i.e., normal and abnormal cases from the features, employing discriminant analysis. An experiment was performed employing 1,200 moire images and 84.72% of the images were classified correctly.

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