Combination of Radiological and Gray Level Co-occurrence Matrix Textural Features Used to Distinguish Solitary Pulmonary Nodules by Computed Tomography
Combination of Radiological and Gray Level Co-occurrence Matrix Textural Features Used to Distinguish Solitary Pulmonary Nodules by Computed Tomography
复制标题
放射学和灰度共生矩阵纹理特征的组合用于通过计算机断层扫描区分孤立性肺结节
DOI：
10.1007/s10278-012-9547-6
复制
发表时间：
2013-08-01
影响因子：
4.4
通讯作者：
Guo, Xiuhua
中科院分区：
文献类型：
--
作者：
Wu, Haifeng;Sun, Tao;Guo, Xiuhua
本研究的目的是探讨CT结合影像学和纹理特征鉴别良恶性孤立性肺结节的方法。从202例（116例恶性，86例良性）患者的2,117张CT切片中提取了13个灰度共生矩阵纹理特征和12个影像学特征。对非线性回归模型进行Lasso型正则化，选择预测特征，并利用BP人工神经网络建立诊断模型。八个放射学和两个纹理特征后，得到的Lasso型正则化程序。仅12个影像学特征就可以达到0.84的ROC曲线下面积（AUC）来区分恶性和良性病变。所选的10个性状将AUC提高到0.91。评价结果表明，选择放射学和纹理特征的方法似乎产生更有效的区分恶性和良性孤立性肺结节的计算机断层扫描。
The objective of this study was to investigate the method of the combination of radiological and textural features for the differentiation of malignant from benign solitary pulmonary nodules by computed tomography. Features including 13 gray level co-occurrence matrix textural features and 12 radiological features were extracted from 2,117 CT slices, which came from 202 (116 malignant and 86 benign) patients. Lasso-type regularization to a nonlinear regression model was applied to select predictive features and a BP artificial neural network was used to build the diagnostic model. Eight radiological and two textural features were obtained after the Lasso-type regularization procedure. Twelve radiological features alone could reach an area under the ROC curve (AUC) of 0.84 in differentiating between malignant and benign lesions. The 10 selected characters improved the AUC to 0.91. The evaluation results showed that the method of selecting radiological and textural features appears to yield more effective in the distinction of malignant from benign solitary pulmonary nodules by computed tomography.