We are developing computerized feature extraction and classification methods to analyze malignant and benign pulmonary nodules in three-dimensional (3-D) thoracic CT images. Surrounding structure features were designed to characterize the relationships between nodules and their surrounding structures such as vessel, bronchi, and pleura. Internal structure features were derived from CT density and 3-D curvatures to characterize the inhomogeneous of CT density distribution inside the nodule. The stepwise linear discriminant classifier was used to select the best feature subset from multidimensional feature spaces. The discriminant scores output from the classifier were analyzed by the receiver operating characteristic (ROC) method and the classification accuracy was quantified by the area, Az, under the ROC curve. We analyzed a data set of 248 pulmonary nodules in this study. The internal structure features (Az=0.88) were more effective than the surrounding structure features (Az=0.69) in distinguishing malignant and benign nodules. The highest classification accuracy (Az=0.94) was obtained in the combined internal and surrounding structure feature space. The improvement was statistically significant in comparison to classification in either the internal structure or the surrounding structure feature space alone. The results of this study indicate the potential of using combined internal and surrounding structure features for computer-aided classification of pulmonary nodules.
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