Ye Ben, Yuan Xixi, Cai Zhanchuan, Lan Ting
Faculty of Information TechnologyMacau University of Science and Technology Macau 999078 China.
IEEE Trans Industr Inform. 2021 Feb 3;17(11):7456-7467. doi: 10.1109/TII.2021.3056386. eCollection 2021 Nov.
Digital image feature recognition is significant to industrial information applications, such as bioengineering, medical diagnosis, and machinery industry. In order to supply an effective and reasonable technology of the severity assessment mission of coronavirus disease (COVID-19), in this article, we propose a new method that identifies rich features of lung infections from a chest computed tomography (CT) image, and then assesses the severity of COVID-19 based on the extracted features. First, in a chest CT image, the lung contours are corrected for the segmentation of bilateral lungs. Then, the lung contours and areas are obtained from the lung regions. Next, the coarseness, contrast, roughness, and entropy texture features are extracted to confirm the COVID-19 infected regions, and then the lesion contours are extracted from the infected regions. Finally, the texture features and V-descriptors are fused as an assessment descriptor for the COVID-19 severity estimation. In the experiments, we show the feature extraction and lung lesion segmentation results based on some typical COVID-19 infected CT images. In the lesion contour reconstruction experiments, the performance of V-descriptors is compared with some different methods, and various feature scores indicate that the proposed assessment descriptor reflects the infected ratio and the density feature of the lesions well, which can estimate the severity of COVID-19 infection more accurately.
数字图像特征识别对生物工程、医学诊断和机械工业等工业信息应用具有重要意义。为了提供一种有效且合理的新型冠状病毒肺炎（COVID-19）严重程度评估技术，在本文中，我们提出了一种新方法，该方法从胸部计算机断层扫描（CT）图像中识别肺部感染的丰富特征，然后基于提取的特征评估COVID-19的严重程度。首先，在胸部CT图像中，对肺轮廓进行校正以用于双侧肺的分割。然后，从肺区域获取肺轮廓和面积。接下来，提取粗糙度、对比度、粗糙度和熵纹理特征以确认COVID-19感染区域，然后从感染区域提取病变轮廓。最后，将纹理特征和V描述符融合作为COVID-19严重程度估计的评估描述符。在实验中，我们展示了基于一些典型COVID-19感染CT图像的特征提取和肺病变分割结果。在病变轮廓重建实验中，将V描述符的性能与一些不同方法进行比较，各种特征分数表明所提出的评估描述符能够很好地反映病变的感染率和密度特征，从而可以更准确地估计COVID-19感染的严重程度。
