Classification of Tumid Lymph Nodes Metastases and Non-Metastases from Lung Cancer in CT Image

Liu Lu; Liu Wan-yu; Chu Chun-yu; Wu Jun; Zhou Yang; Zhang Hong-xia; Bao Jie

doi:10.3724/SP.J.1146.2009.00699

Volume 31 Issue 10

Dec. 2010

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Article Navigation > Journal of Electronics & Information Technology > 2009 > 31(10): 2476-2482

Liu Lu, Liu Wan-yu, Chu Chun-yu, Wu Jun, Zhou Yang, Zhang Hong-xia, Bao Jie. Classification of Tumid Lymph Nodes Metastases and Non-Metastases from Lung Cancer in CT Image[J]. Journal of Electronics & Information Technology, 2009, 31(10): 2476-2482. doi: 10.3724/SP.J.1146.2009.00699

Citation:

Liu Lu, Liu Wan-yu, Chu Chun-yu, Wu Jun, Zhou Yang, Zhang Hong-xia, Bao Jie. Classification of Tumid Lymph Nodes Metastases and Non-Metastases from Lung Cancer in CT Image[J]. Journal of Electronics & Information Technology, 2009, 31(10): 2476-2482. doi: 10.3724/SP.J.1146.2009.00699

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Classification of Tumid Lymph Nodes Metastases and Non-Metastases from Lung Cancer in CT Image

doi: 10.3724/SP.J.1146.2009.00699 cstr: 32379.14.SP.J.1146.2009.00699

Received Date: 2009-05-11
Rev Recd Date: 2009-07-13
Publish Date: 2009-10-19

Abstract

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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