A computer-aided diagnosis system for lung nodule detection in chest radiographs using a two-stage classification method based on radial gradient and template matching

In this paper we propose a scheme for automated detection of lung nodules in chest radiographs. The proposed scheme first segments lungs in a chest image using an active shape model. Next, the scheme detects initial nodule candidates by using a method previously reported by the authors. After that, the proposed scheme classifies nodule candidates into nodules and false positives by using a two-stage classification method proposed in this paper. For performance evaluation of the proposed nodule detection scheme, we made experiments using 125 images with nodules in the JSRT database which is a public database. We created 40 data sets by 40 randomized selection of 80 training images and 45 test images from the 125 images. As the result of experiments using these 40 data sets, the proposed scheme gave 6.6, 7.6, and 9.1 false positives per image for sensitivity values of 60.1, 64.1, and 69.7% on the average of 40 data sets. The time needed by the proposed scheme was 8.2 seconds per image on the average of 40 data sets using 3.3GHz Intel PC.