In order to improve the accuracy of the diagnosis with medical signs in medical imaging diagnostics,a computer-aided diagnosis system is developed quickly and accurately to find out the differentce of benign and malignant Solitary Pulmonary Nodwles(SPNs) in chest CT images based the image features of SPNs.Firstly,SPNs are extracted from chest CT images using the interactive segmentation,and the multi-resolution histograms of SPNs are directly calculated to receive a high-dimensional feature sample set with spatial information of SPNs.Then,the classifier for differentiating benign and malignant SPN is constructed by using a Support Vector Mechine(SVM).Finally,the performance of classification is evaluated by testing the trained SVM with the test sample set.The test results of 214 cases show that it takes 4.83 s for computing 768 dimensional features of 240 SPNs and 2.24 s for training and testing the SVM classifier.The receiver operating characteristic (ROC) analysis of classification performance of the proposed approach shows that the sensitivity is 73.33%,specificity is 70%,accuracy is 71.67%,and the area under curve (AUC) is nearly 0.786 4.Obtained results show that the image spatial information can effectively express the characteristics of SPNs.The system classification accuracy of benign and malignant SPNs is up to 71.67% without medical signs,and the classification speed is about 50 times faster than that of traditional texture methods.It provides a feasible,simple and objective method for solving the problem in medical imaging diagnosis of the SPNs.