ABSTRACT

In recent years, the incidence of lung cancer has been increasing. Lung cancer detection is based on computed tomography (CT) imaging of the lung area to determine whether there are pulmonary nodules. And then judge what’s good and what’s bad. However, due to the traditional way of manual reading and lack of experience and other problems. This leads to visual fatigue and misdiagnosis and missed diagnosis. In order to detect pulmonary nodules early and accurately, a new assistant diagnosis method for pulmonary nodules is proposed. Firstly, the image is preprocessed and denoised by median filter, the lung parenchyma is segmented by random walk algorithm and the region of interest is extracted, and then, according to the continuity of the CT slices, the texture feature extraction method of pulmonary nodules based on volume local direction ternary pattern is used to extract the features. Finally, the pulmonary nodules are identified and classified by the assistant diagnosis model of pulmonary nodules based on Stacking algorithm. In order to illustrate the validity of the diagnosis model, the experiments are carried out by cross-validation of ten folds. Experiments using data from LIDC database show that the accuracy, sensitivity and specificity of the proposed method are 82.2%, 85.7%, and 78.8%, respectively. Texture Recognition method based on volume vocal direction ternary pattern is feasible for the identification of pulmonary nodules and provides a reference value for doctor-assisted diagnosis.

摘要

近年来，肺癌的发病率呈上升趋势。肺癌检测是基于肺区域的计算机断层扫描 (CT) 成像来确定是否存在肺结节。然后判断什么是好什么是坏。但是由于传统的手工阅读方式和经验不足等问题。这会导致视觉疲劳和误诊漏诊。为了早期准确地发现肺结节，提出了一种新的肺结节辅助诊断方法。首先对图像进行中值滤波预处理和去噪，通过随机游走算法对肺实质进行分割并提取感兴趣区域，然后根据CT断层的连续性，采用基于体积局部方向三元模式的肺结节纹理特征提取方法提取特征。最后，通过基于Stacking算法的肺结节辅助诊断模型对肺结节进行识别和分类。为了说明诊断模型的有效性，通过十次交叉验证进行实验。使用LIDC数据库数据的实验表明，所提出方法的准确度、灵敏度和特异性分别为82.2%、85.7%和78.8%。基于音量声向三元模式的纹理识别方法对于肺结节的识别是可行的，为医生辅助诊断提供了参考价值。通过基于Stacking算法的肺结节辅助诊断模型对肺结节进行识别和分类。为了说明诊断模型的有效性，通过十次交叉验证进行实验。使用LIDC数据库数据的实验表明，所提出方法的准确度、灵敏度和特异性分别为82.2%、85.7%和78.8%。基于音量声向三元模式的纹理识别方法对于肺结节的识别是可行的，为医生辅助诊断提供了参考价值。通过基于Stacking算法的肺结节辅助诊断模型对肺结节进行识别和分类。为了说明诊断模型的有效性，通过十次交叉验证进行实验。使用LIDC数据库数据的实验表明，所提出方法的准确度、灵敏度和特异性分别为82.2%、85.7%和78.8%。基于音量声向三元模式的纹理识别方法对于肺结节的识别是可行的，为医生辅助诊断提供了参考价值。使用LIDC数据库数据的实验表明，所提出方法的准确度、灵敏度和特异性分别为82.2%、85.7%和78.8%。基于音量声向三元模式的纹理识别方法对于肺结节的识别是可行的，为医生辅助诊断提供了参考价值。使用LIDC数据库数据的实验表明，所提出方法的准确度、灵敏度和特异性分别为82.2%、85.7%和78.8%。基于音量声向三元模式的纹理识别方法对于肺结节的识别是可行的，为医生辅助诊断提供了参考价值。