Contourlet textual features: improving the diagnosis of solitary pulmonary nodules in two dimensional CT images.
Contourlet textual features: improving the diagnosis of solitary pulmonary nodules in two dimensional CT images.
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Contourlet 文本特征：提高二维 CT 图像中孤立性肺结节的诊断
DOI：
10.1371/journal.pone.0108465
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Guo X
中科院分区：
文献类型：
--
作者：
Wang J;Sun T;Gao N;Menon DD;Luo Y;Gao Q;Li X;Wang W;Zhu H;Lv P;Liang Z;Tao L;Liu X;Guo X
目的探讨二维CT图像上孤立性肺结节轮廓波纹理特征在肺癌诊断中的价值。资料与方法收集336例患者的6,299张CT图像，其中良性肺结节84例（男50例，女34例），1,454张;恶性肺结节252例（男150例，女102例），4,845张。除此之外，还收集了19个患者信息类别，其中包括7个人口统计学参数和12个形态学特征。一个contourlet被用来提取14种类型的纹理特征。然后，这些被用来建立三个支持向量机模型。一个包括一个数据库构建的十九个收集的患者信息类别，另一个包括轮廓纹理特征和第三个包含两组信息。以灵敏度、特异性、准确性、曲线下面积（AUC）、精密度、约登指数（Youden index）和F-测量（F-measure）为评价标准，采用10倍交叉验证对3个数据库的诊断结果进行评价。此外，采用合成少数过采样技术（SMOTE）对不平衡数据进行预处理。结果使用包含纹理特征和患者信息的数据库，敏感性、特异性、准确性、AUC、精密度、Youden指数和F-测量分别为：0.95、0.71、0.89、0.89、0.92、0.66和0.93。这些结果高于使用没有纹理特征的数据库（分别为0.82、0.47、0.74、0.67、0.84、0.29和0.83）以及仅包括纹理特征的数据库（分别为0.81、0.64、0.67、0.72、0.88、0.44和0.85）得到的结果。使用SMOTE作为预处理程序，生成新的平衡数据库，包括5，816个良性ROI和5，815个恶性ROI的观察结果，准确率为0.93。结论结合CT图像轮廓波纹理特征和患者个人资料，可提高肺癌的诊断率。
Objective To determine the value of contourlet textural features obtained from solitary pulmonary nodules in two dimensional CT images used in diagnoses of lung cancer. Materials and Methods A total of 6,299 CT images were acquired from 336 patients, with 1,454 benign pulmonary nodule images from 84 patients (50 male, 34 female) and 4,845 malignant from 252 patients (150 male, 102 female). Further to this, nineteen patient information categories, which included seven demographic parameters and twelve morphological features, were also collected. A contourlet was used to extract fourteen types of textural features. These were then used to establish three support vector machine models. One comprised a database constructed of nineteen collected patient information categories, another included contourlet textural features and the third one contained both sets of information. Ten-fold cross-validation was used to evaluate the diagnosis results for the three databases, with sensitivity, specificity, accuracy, the area under the curve (AUC), precision, Youden index, and F-measure were used as the assessment criteria. In addition, the synthetic minority over-sampling technique (SMOTE) was used to preprocess the unbalanced data. Results Using a database containing textural features and patient information, sensitivity, specificity, accuracy, AUC, precision, Youden index, and F-measure were: 0.95, 0.71, 0.89, 0.89, 0.92, 0.66, and 0.93 respectively. These results were higher than results derived using the database without textural features (0.82, 0.47, 0.74, 0.67, 0.84, 0.29, and 0.83 respectively) as well as the database comprising only textural features (0.81, 0.64, 0.67, 0.72, 0.88, 0.44, and 0.85 respectively). Using the SMOTE as a pre-processing procedure, new balanced database generated, including observations of 5,816 benign ROIs and 5,815 malignant ROIs, and accuracy was 0.93. Conclusion Our results indicate that the combined contourlet textural features of solitary pulmonary nodules in CT images with patient profile information could potentially improve the diagnosis of lung cancer.
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