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Multi-scale curvature indexes are introduced to characterize the internal intensity structure of pulmonary nodules in thin-section CT images. This approach makes use of shape index, curvedness, and CT density to represent locally each voxel constructing the three-dimensional (3D) pulmonary nodule image. Using features extracted from the histogram of the multi-scale curvature indexes and CT density, the pulmonary nodules are discriminated between benign and malignant cases by the linear discriminant classifier. In this study a data set of 128 pulmonary nodules is analyzed to investigate which scale provides high classification accuracy between malignant and benign nodules. Additionally, the extracted features are evaluated for four different regions: (i) entire 3D pulmonary nodule; (ii) core region in the 3D pulmonary nodule; (iii) complement of the-core region in the 3D pulmonary nodule; (iv) neighborhood region surrounding the 3D pulmonary nodule. The effectiveness of the multi-scale curvature indexes in a computer-aided differential diagnosis is demonstrated by receiver operating characteristic (ROC) analysis.