This paper focuses on characterizing the internal intensity structure of pulmonary nodules in thin-section CT images for classification between benign and malignant nodules. This approach makes use of shape index curvedness, and CT density to represent locally each voxel constructing the three-dimensional (3D) pulmonary nodule image. From the distribution of shape index, curvedness, and CT density over the 3D pulmonary nodule image a set of histogram features, and 3D texture features is computed to classify benign and malignant 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.
['Yoshiki Kawata', 'Noboru Niki', 'Hironobu Ohmatsu', 'Masahiko Kusumoto', 'Ryutaro Kakinuma', 'Kensaku Mori', 'Hiroyuki Nishiyama', 'Katsumi Eguchi', 'Masahiro Kaneko', 'Noriyuki Moriyama']
Computer aided differential diagnosis of pulmonary nodules using curvature based analysis