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| Classification of Tumid Lymph Nodes Metastases and Non-Metastases from Lung Cancer in CT Image |
| Liu Lu①; Liu Wan-yu①; Chu Chun-yu②; Wu Jun③; Zhou Yang③; Zhang Hong-xia③; Bao Jie① |
| ①HIT-INSA Sino-French Research Center for Biomedical Imaging, Harbin Institute of Technology, Harbin 150001, China; ②School of Automation, Harbin University of Science and Technology, Harbin 150080, China; ③The Tumor Hospital of Harbin Medical University, Harbin 150081, China |
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Abstract In order to solve the low accuracy diagnosis of metastases and non-metastases tumid lymph nodes in the lung cancer N stage with chest CT images, effective image features of lymph nodes need to be found for quickly and accurately differentiating metastases and non-metastases tumid lymph nodes. First, tumid lymph nodes are extracted from chest CT images using interactive segmentation. Second, the multi-resolution histograms of tumid lymph nodes are directly calculated to receive a high-dimensional features sample set with spatial information. Then the classifier for differentiating metastases and non-metastases tumid lymph nodes 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. The test results by 96 cases show that it takes 1.91 s for computing 200 dimensional features of 100 lymph nodes, 1.36 s for training and testing the SVM classifier. Receiver Operating Characteristic (ROC) analysis of the classification performance shows that the sensitivity is 76%, specificity is 64%, accuracy is 70%, and the Area Under Curve (AUC) is nearly 0.6525. Image spatial information can effectively express the characteristics of lymph nodes, the classification accuracy of metastases and non-metastases tumid lymph nodes is up to 70% without medical signs, and the classification speed is about 10 times than traditional texture methods. It provides a feasible, simple, objective method for improving the accuracy of the lung cancer N stage in medical imaging diagnosis.
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Received: 11 May 2009
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2009, Vol. 31 
