针对现有的肺结节良恶性分类算法存在分类准确率不高的问题，文中提出了一 种基于放射影像组学和随机森林算法的肺结节良恶性分类算法． 首先，提出了一种新的多 尺度圆形滤波，用于对肺结节进行增强; 其次，采用阈值法、形状指数和纹理特征自动获取 种子点，并将种子点注入到随机游走算法中，以实现对肺结节的准确分割; 然后，对分割的 肺结节进行灰度、纹理、形状、小波和临床表征特征的提取; 最后，采用随机森林构造肺结 节良恶性的预测模型，并使用数据库 LIDC 对预测模型进行训练． 实验结果表明，文中提 出的算法对肺结节良恶性具有较高的分类性能，准确率、敏感性和特异性分别为 94%、 92%和 94%． 

Currently, lung cancer is one of the leading causes of morbidity and mortality worldwide. Accurate classification of benign and malignant pulmonary nodules will help the clinicians to accurately diagnose in stages and make the optimal treatment planning in time. To address the low accuracy rate problem for the existing classification algorithms of benign and malignant pulmonary nodules, a classification algorithm based on radiomic feature and random forests model for the classification of benign and malignant pulmonary nodules is proposed in this paper. Firstly, a novel multiscale dot enhancement filter is proposed for pulmonary nodule enhancement. Then, seeds are accurately acquired based on shape index and texture features in enhanced pulmonary nodules, and the acquired seeds are injected into the RW algorithm to accurately segment pulmonary nodules. Secondly, the intensity, texture, shape, wavelet, and clinical features are extracted in the segmented pulmonary nodules. Finally, random forests (RFs) are employed to build the predictive model for classifying benign and malignant pulmonary nodules. The LIDC database is used to train the predictive model. The experiments demonstrate the effectiveness of the proposed algorithm for the classification of benign and malignant pulmonary nodules. The accurate classification results can provide a reference for the clinical diagnosis of lung cancer and a good prognostic value.