Objective:　To　explore　the　diagnostic　value　for　benign　and　malignant　pulmonary　nodules　using　the　wavelet　texture　features　based　on　nonsubsampled　dual-tree　complex　contourlet　transform　(NSDTCT).　Methods　Texture　parameters　based　on　NSDTCT　and　Contourlet　transform　were　extracted　from　CT　images　of　patients　with　pulmonary　nodules.　Dimension　reduction　of　texture　features　was　conducted　with　univariate　analysis　and　Lasso　regression.　The　support　vector　machine　classifiers　based　on　these　texture　features　for　benign　and　malignant　pulmonary　nodules　were　constructed.　ROC　analysis　was　applied　to　compare　the　two　texture　extraction　methods.　Results　For　NSDTCT　based　features,　the　model　based　on　the　least　number　of　NSDTCT　texture　after　Lasso　dimension　reduction　was　of　excellent　performance,　with　the　accuracy　of　98.37%　in　diagnosing　benign　and　malignant　lung　nodules,　and　the　AUC　was　1.00.　For　Contourlet　transform　based　features,　the　model　with　all　extracted　texture　features　performed　well,　with　the　accuracy　of　56.05%,　and　the　AUC　was　0.73.　There　was　significant　difference　of　AUC　of　ROC　curve　between　the　two　models　(Z=6.430,　P＜0.001).　Conclusion:　NSDTCT　texture　analysis　method　has　good　performance　for　diagnosing　lung　cancer　with　high　classification　accuracy.　Copyright　©　2019　by　the　Press　of　Chinese　Journal　of　Medical　Imaging　and　Technology.