Texture feature from chest CT images for malignancy assessment of pulmonary nodules has become an un-ignored and efficient factor in Computer-Aided Diagnosis (CADx). In this paper, we focus on extracting as fewer as needed efficient texture features, which can be combined with other classical features (e.g. size, shape, growing rate, etc.) for assisting lung nodule diagnosis. Based on a typical calculation algorithm of texture features, namely Haralick features achieved from the gray-tone spatial-dependence matrices, we calculated two dimensional (2D) and three dimensional (3D) Haralick features from the CT images of 905 nodules. All of the CT images were downloaded from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), which is the largest public chest database. 3D Haralick feature model of thirteen directions contains more information from the relationships on the neighbor voxels of different slices than 2D features from only four directions. After comparing the efficiencies of 2D and 3D Haralick features applied on the diagnosis of nodules, principal component analysis (PCA) algorithm was used to extract as fewer as needed efficient texture features. To achieve an objective assessment of the texture features, the support vector machine classifier was trained and tested repeatedly for one hundred times. And the statistical results of the classification experiments were described by an average receiver operating characteristic (ROC) curve. The mean value (0.8776) of the area under the ROC curves in our experiments can show that the two extracted 3D Haralick projected features have the potential to assist the classification of benign and malignant nodules.