Objective: In order to break through the bottleneck of low accuracy diagnosis with medical signs in medical imaging diagnostics

effective image features of solitary pulmonary nodules (SPN) need to be found for the computer-aided diagnosis system quickly and accurately differentiating benign and malignant SPNs in chest CT images. Method: First

SPNs are extracted from chest CT images using interactive segmentation. Second

the multi-resolution histograms of SPNs are directly calculated to receive a high-dimensional features sample set with spatial information of SPNs.Then the classifier for differentiating benign and malignant SPN is constructed with making full use the advantage of SVM which is good at dealing with high dimensional data sets. Finally

the performance of classification is evaluated by testing the trained SVM with the test sample set. Result: The test results by 214 cases show that it takes 4.83s for computing 768 dimensional features of 240 SPNs

2.24s for training and testing the SVM classifier. 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 0.7864. Conclusion: Image spatial information can effectively express the characteristics of SPN

the system classification accuracy of benign and malignant SPNs is up to 71.67% without medical signs

and the classification velocity is about 50 times than traditional texture methods. It provides a feasible

simple

objective method for solving the problem in medical imaging diagnosis of the SPN.