D-Y Kim1, J-H Kim, S-M Noh, J-W Park. 1. Department of Information and Communication Engineering, Chungnam National University, Taejon, South Korea. dykim@ns.kopec.co.kr
Abstract
PURPOSE: Automated methods for the detection of pulmonary nodules and nodule volume calculation on CT are described. MATERIAL AND METHODS: Gray-level threshold methods were used to segment the thorax from the background and then the lung parenchyma from the thoracic wall and mediastinum. A deformable model was applied to segment the lung boundaries, and the segmentation results were compared with the thresholding method. The lesions that had high gray values were extracted from the segmented lung parenchyma. The selected lesions included nodules, blood vessels and partial volume effects. The discriminating features such as size, solid shape, average, standard deviation and correlation coefficient of selected lesions were used to distinguish true nodules from pseudolesions. With texture features of true nodules, the contour-following method, which tracks the segmented lung boundaries, was applied to detect juxtapleural nodules that were contiguous to the pleural surface. Volume and circularity calculations were performed for each identified nodule. The identified nodules were sorted in descending order of volume. These methods were applied to 827 image slices of 24 cases. RESULTS: Computer-aided diagnosis gave a nodule detection sensitivity of 96% and no false-positive findings. CONCLUSION: The computer-aided diagnosis scheme was useful for pulmonary nodule detection and gave characteristics of detected nodules.
PURPOSE: Automated methods for the detection of pulmonary nodules and nodule volume calculation on CT are described. MATERIAL AND METHODS: Gray-level threshold methods were used to segment the thorax from the background and then the lung parenchyma from the thoracic wall and mediastinum. A deformable model was applied to segment the lung boundaries, and the segmentation results were compared with the thresholding method. The lesions that had high gray values were extracted from the segmented lung parenchyma. The selected lesions included nodules, blood vessels and partial volume effects. The discriminating features such as size, solid shape, average, standard deviation and correlation coefficient of selected lesions were used to distinguish true nodules from pseudolesions. With texture features of true nodules, the contour-following method, which tracks the segmented lung boundaries, was applied to detect juxtapleural nodules that were contiguous to the pleural surface. Volume and circularity calculations were performed for each identified nodule. The identified nodules were sorted in descending order of volume. These methods were applied to 827 image slices of 24 cases. RESULTS: Computer-aided diagnosis gave a nodule detection sensitivity of 96% and no false-positive findings. CONCLUSION: The computer-aided diagnosis scheme was useful for pulmonary nodule detection and gave characteristics of detected nodules.