[Improvement of overall performance for lung nodule detection in chest CT images].

We have developed an automated computerized schema for the detection of lung nodules in 3D CT images obtained by helical CT. In our previous schema, linear discriminant analysis (LDA) and a rule-based method with 53 image features were employed in order to reduce false positives. However, several false positives have remained. Therefore, in this study, we improved the false-positive reduction technique by using the edge image and radial image analysis. Overall performance for the detection of lung nodules was greatly improved. Sensitivity was higher than that of our previous study. Moreover, we evaluated the overall performance of the new scheme by using 69 cases acquired from four hospitals. The average number of false positives was 5.2 per case at a percent sensitivity of 95.8%. Our new scheme would assist in the detection of early lung cancer.

[Development of computer-aided diagnostic system for detection of lung nodules in three-dimensional computed tomography images].

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.