| 马圆,韩鸿毅,孙燕北,梁志刚,郭秀花.基于非下采样双树复轮廓波变换的小波纹理特征识别肺良恶性结节CT图像[J].中国医学影像技术,2019,35(2):272~276 |
| 基于非下采样双树复轮廓波变换的小波纹理特征识别肺良恶性结节CT图像 |
| Application of nonsubsampled dual-tree complex contourlet transform based wavelet texture features of CT images in identification of benign and malignant pulmonary nodules |
| 投稿时间：2018-05-30 修订日期：2018-09-20 |
| DOI：10.13929/j.1003-3289.201805159 |
| 中文关键词: 非下采样双树复轮廓波变换 肺肿瘤 支持向量机 体层摄影术,X线计算机 |
| 英文关键词:nonsubsampled dual-tree complex contourlet transform lung neoplasmas support vector machine tomography, X-ray computed |
| 基金项目:国家自然科学基金（81773542）、北京市教委科技计划重点项目（KZ201810025031）。 |
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| 中文摘要: |
| 目的 观察基于非下采样双树复轮廓波变换（NSDTCT）的小波纹理特征在识别肺良恶性结节CT图像中的应用价值。方法 从肺结节患者的CT图像中分别提取基于NSDTCT和基于Contourlet变换的小波纹理参数，对高维纹理参数采用单因素分析、Lasso回归等方法进行降维。对降维后的纹理参数分别构建诊断良恶性肺结节的支持向量机分类诊断模型，绘制ROC曲线，比较2种方法的诊断效能。结果 采用NSDTCT方法，基于经Lasso降维且自变量数目较少的纹理参数构建的诊断模型分类效果最好，判断良恶性肺结节的准确率为98.37%，AUC为1.00；采用Contourlet变换方法，基于全部提取纹理参数构建的模型分类效果最好，诊断准确率为56.05%，AUC为0.73；2个模型的ROC曲线的AUC差异有统计学意义（Z=6.430，P<0.001）。结论 基于NSDTCT的纹理分析方法对判断良恶性肺结节的准确性较高。 |
| 英文摘要: |
| Objective To explore the diagnostic value for benign and malignant pulmonary nodules using the wavelet texture features based on nonsubsampled dual-tree complex contourlet transform (NSDTCT). Methods Texture parameters based on NSDTCT and Contourlet transform were extracted from CT images of patients with pulmonary nodules. Dimension reduction of texture features was conducted with univariate analysis and Lasso regression. The support vector machine classifiers based on these texture features for benign and malignant pulmonary nodules were constructed. ROC analysis was applied to compare the two texture extraction methods. Results For NSDTCT based features, the model based on the least number of NSDTCT texture after Lasso dimension reduction was of excellent performance, with the accuracy of 98.37% in diagnosing benign and malignant lung nodules, and the AUC was 1.00. For Contourlet transform based features, the model with all extracted texture features performed well, with the accuracy of 56.05%, and the AUC was 0.73. There was significant difference of AUC of ROC curve between the two models (Z=6.430, P<0.001). Conclusion NSDTCT texture analysis method has good performance for diagnosing lung cancer with high classification accuracy. |
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