@article{oai:kyutech.repo.nii.ac.jp:00001514,
 author = {Kim, Hyungseop and 金, 亨燮 and Tan, Joo kooi and タン, ジュークイ and 石川, 聖二 and Ishikawa, Seiji and Otsuka, Yoshinori and 清水, 尚 and Shimizu,  Hisashi and 四宮, 孝史 and Shinomiya,  Takashi},
 issue = {1},
 journal = {画像電子学会誌, Journal of the Institute of Image Electronics Engineers of Japan},
 month = {Jan},
 note = {本稿では，モアレ像からの脊柱側腎症自動識別法について述べる.脊柱側腎症は脊柱が左右に管曲する病気で，主に，小中学生を中心として発生する．人背面モアレ像は正常例であればほぼ左右対称を示しているが，異常例ではモアレ縞のひずみによる左右非対称が現れる．そこで，脊柱側撃症のもつ非対称の特徴を利用して，モアレ画像から対称性解析を行うことにより，脊柱側管症の自動識別を行う．提案法では，図形の近似的対称性解析法により対称基準を求め，対称基準を境とする左右領域内の重心位置のずれと濃度差の分布の違いを基に，階層型ニューラルネットワーク(ANN)やサポートベクタマシン(SVM)による自動識別を行う．提案法を用いて1,200例の実モアレ画像を三つのデータ群に分け，Leave-one methodによる分類実験を行った結果，ANNでは90.3%，SVMでは85.3%の平均識別率を得た．, In this paper, we propose a new method for automatic classification of spinal deformity from moiré topographic images by using four asymmetric features on right- and left-hand side of human back. Spinal deformity is one of serious disease and it is mainly suffer by teenagers during their growth stage particularly fifth year in the elementary school to second year in the middle school. In the mass school screening, a moiré method has been proposed which takes moiré topographic images of human subject backs and checks its symmetry/asymmetry of the moiré patterns in a two-dimensional way. We perform the proposed method to 1200 real moiré images in the classification employing Artificial Neural Networks based on back propagation and Support Vector Machines. As a result, on the total average, classification rate of 90.3%, and 85.3% were achieved in the ANN, SVM, respectively.},
 pages = {57--62},
 title = {左右非対称の特徴を用いたモアレ画像からの脊柱側彎症自動識別},
 volume = {37},
 year = {2008},
 yomi = {イシカワ, セイジ}
}