胸部三次元CT画像における結節状陰影の自動検出法の開発

概要

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We have developed an automated computerized method for the detection of lung nodules in three-dimensional (3D) computed tomography (CT) images obtained by helical CT. In this scheme, a lung segmentation technique for the determination of the nodule search area is performed based on a gray-level thresholding technique. To enhance lung nodules, we employed the 3D cross-correlation method by using a 3D Gaussian template with zero-surrounding as a model of lung nodule. False positives are then eliminated by using a rule-base with 53 features. For further reduction of false positives, we performed linear discriminant analysis using these 53 features. The average number of false positives was 6.7 per case at a percent sensitivity of 85.0%. This computerized scheme will be useful to radiologists by providing a "second opinion" in case of possible early lung cancer.
社団法人日本放射線技術学会の論文
2006-04-20