Curvature based characterization of shape and internal intensity structure for classification of pulmonary nodules using thin-section CT images

This paper presents a curvature based approach to characterize the internal intensity structure of pulmonary nodules in thin-section CT images. This approach makes use of shape index, curvedness, and CT value to represent locally each voxel in a three-dimensional (3-D) pulmonary nodule image. From the distribution of shape index, curvedness, and CT value over the 3-D pulmonary nodule image a set of 3-D moment features, histogram features, and 3-D texture features is computed to classify benign and malignant pulmonary nodules. Linear discriminant analysis is used for classification and a receiver operating characteristic (ROC) analysis is used to evaluate the classification accuracy. The potential usefulness of the curvature based features in the computer-aided differential diagnosis is demonstrated by using ROC curves as the performance measure.