CT图像中肿大淋巴结肺癌转移分类方法  

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

作　　者：刘露[1] 刘宛予[1] 楚春雨[2] 吴军[3] 周洋[3] 张红霞[3] 鲍劼[1] 

机构地区：[1]哈尔滨工业大学HIT-INSA中法生物医学图像联合研究中心,哈尔滨150001 [2]哈尔滨理工大学自动化学院,哈尔滨150080 [3]哈尔滨医科大学附属肿瘤医院,哈尔滨150081

出　　处：《电子与信息学报》2009年第10期2476-2482,共7页Journal of Electronics & Information Technology

基　　金：国家国际科技合作重大专项(2007DFB30320);国家自然科学基金(60777004);黑龙江省教育厅科技计划项目(11531048)资助课题

摘　　要：为解决肺癌N分期中胸部CT难于对肿大淋巴结是否癌转移进行评价的问题,寻求能够有效表示淋巴结病理特性的图像特征,实现对肿大淋巴结癌转移快速准确地判别。该文采取交互式分割从CT图像中提取出肿大淋巴结;直接计算淋巴结的多分辨率直方图得到200维空间信息特征样本集;利用具有处理高维数据集优势的支持向量机(SVM)构造分类器;用测试集对经训练的SVM分类器进行测试以评价分类性能。经96例病例实验结果表明:100个淋巴结图像的200维特征计算用时1.91s,SVM分类器训练测试用时1.36s,敏感性76%,特异性64%,准确度70%,接受者操作特性曲线(ROC)下面积(AUC)0.6525。高维图像空间信息特征能够有效表示淋巴结特性;没有考虑医学征象进行肿大淋巴结癌转移定性诊断的准确度就达到了70%,同时分类速度比传统纹理算法提高了约10倍。In order to solve the low accuracy diagnosis of metastases and non-metastases tumid lymph nodes in the lung cancer Nstage 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.

关 键 词：肺癌N分期 CT图像 肿大淋巴结 多分辨率直方图 支持向量机(SVM) 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] R445.3[自动化与计算机技术—计算机科学与技术]