Computer-aided differential diagnosis of pulmonary nodules based on a hybrid classification approach

摘要

We are developing computerized feature extraction and classification methods to analyze malignant and benign pulmonary nodules in 3D thoracic CT images. Internal structure features were derived form CT density and 3D curvatures to characterize the inhomogeneous of CT density distribution inside the nodule. In the classification step, we combined an unsupervised k-means clustering (KMC) procedure and a supervised linear discriminate (LD) classifier. The KMC procedure classified the sample nodules into two classes by using the mean CT density values for two different regions such as a core region and a complement of the core region in 3D nodule image. The LD classifier was designed for each class by using internal structure features. The forward stepwise procedure was used to select the best feature subset from multi-dimensional feature spaces. The discriminant scores output form the classifier were analyzed by receiver operating characteristic (ROC) method and the classification accuracy was quantified by the area, Ax, under the ROC curve. We analyzed a data set of 248 pulmonary nodules in this study. The hybrid classifier was more effective than the LD classifier alone in distinguishing malignant and benign nodules. The improvement was statistically significant in comparison to classification in the LD classifier alone. The results of this study indicate the potential of combining the KMC procedure and the LD classifier for computer-aided classification of pulmonary nodules.