Objective To reduce the dimension of the high-dimensional texture parameters of PET/CT images and to improve the accuracy of classification by building the K-nearest neighbor（KNN） classifier based on different texture features. Methods The study retrospectively collected 52 cases with pulmonary nodules who underwent ¹⁸F -FDG PET/CT from department of Nuclear Medicine, Xuanwu Hospital Capital Medical University. Co-occurrence matrix texture features were extracted from the contourlet transformed PET/CT images. Univariate analysis was applied first to reduce dimensionality of texture features according to c value before principle components analysis. Principal components of texture features from selected texture features were extracted by PCA. We built the KNN classifier for original textures, selected textures and principle components respectively to distinguish benign and malignant nodules, comparing the efficacy of models based on the evaluation indices such as accuracy, sensitivity, specificity and AUC. Results 1344 original texture features were extracted from the region of interest of PET/CT images, from which 89 texture features were selected. Eleven principal components were extracted through the PCA procedure. The accuracy of KNN classifiers based on principal components, selected textures and original textures are 0.614，0.579 and 0.263 with AUC of 0.645，0.610，0.515 respectively. Conclusion The KNN classifier based on the texture of principal components is the best one among the classifiers based on original texture features, the selected texture features through univariate analysis and the texture of principal components.