Department of CT, Hengshui People's Hospital, Hengshui, 053000, Hebei, China.
Department of Pathology, Hengshui People's Hospital, Hengshui, 053000, Hebei, China.
J Infect Public Health. 2021 Mar;14(3):418-422. doi: 10.1016/j.jiph.2019.08.001. Epub 2019 Aug 23.
To explore the quality of high-resolution CT images information in the evaluation of pulmonary nodule interface and internal structure of nodules in lung tissue, as well as the value of early diagnosis of lung cancer associated with infection, high-resolution CT images were used as the research object. Through the analysis of the computerized detection and diagnosis (Computer-Aided Diagnosis (CAD)) of lung cancer, the high-resolution CT was further explored in the process of clinical imaging doctors in the diagnosis of lung cancer, and more conditions were created for the application of medical image processing in the early diagnosis of lung cancer. The research results show that CAD can automatically and accurately complete the automatic segmentation of the lung region in the CT image by applying the automatic segmentation algorithm for a series of processing and analysis of the CT image, that is, generating high-resolution CT images. It can enhance the pulmonary nodules in CT images and improve the accuracy of lung nodule detection, which is of great value in the diagnosis of early lung cancer. CAD diagnosis of lung lesions based on high-resolution CT images is studied, which can provide reference for imaging physicians to diagnose early lung cancer. However, in the automatic identification of benign and malignant lesions in the lungs, it is necessary to further improve the analysis function of similar nodules, which will be an important step for humans in the diagnosis and treatment of diseases.
为了探索肺部结节界面和内部结构的高分辨率 CT 图像信息在肺癌伴感染早期诊断中的应用价值，将高分辨率 CT 图像作为研究对象。通过肺癌计算机化检测和诊断（计算机辅助诊断（CAD））的分析，进一步探讨了临床影像医生在肺癌诊断中应用高分辨率 CT 的情况，为医学图像处理在肺癌早期诊断中的应用创造了更多条件。研究结果表明，CAD 可以通过应用自动分割算法对 CT 图像进行一系列处理和分析，自动、准确地完成 CT 图像中肺部区域的自动分割，即生成高分辨率 CT 图像。它可以增强 CT 图像中的肺结节，提高肺结节检测的准确性，对早期肺癌的诊断具有重要价值。基于高分辨率 CT 图像的 CAD 诊断对影像医师早期诊断肺癌具有重要参考价值。但是，在肺部良恶性病变的自动识别中，还需要进一步提高对相似结节的分析功能，这将是人类在疾病诊断和治疗中的重要一步。
