Tang Lu, Tian Chuangeng, Meng Yankai, Xu Kai
School of Medical Imaging Xuzhou Medical University Xuzhou China.
School of Information and Electrical Engineering Xuzhou University of Technology Xuzhou China.
Int J Imaging Syst Technol. 2021 Sep;31(3):1120-1127. doi: 10.1002/ima.22583. Epub 2021 Apr 28.
Blur is a key property in the perception of COVID-19 computed tomography (CT) image manifestations. Typically, blur causes edge extension, which brings shape changes in infection regions. Tchebichef moments (TM) have been verified efficiently in shape representation. Intuitively, disease progression of same patient over time during the treatment is represented as different blur degrees of infection regions, since different blur degrees cause the magnitudes change of TM on infection regions image, blur of infection regions can be captured by TM. With the above observation, a longitudinal objective quantitative evaluation method for COVID-19 disease progression based on TM is proposed. COVID-19 disease progression CT image database (COVID-19 DPID) is built to employ radiologist subjective ratings and manual contouring, which can test and compare disease progression on the CT images acquired from the same patient over time. Then the images are preprocessed, including lung automatic segmentation, longitudinal registration, slice fusion, and a fused slice image with region of interest (ROI) is obtained. Next, the gradient of a fused ROI image is calculated to represent the shape. The gradient image of fused ROI is separated into same size blocks, a block energy is calculated as quadratic sum of non-direct current moment values. Finally, the objective assessment score is obtained by TM energy-normalized applying block variances. We have conducted experiment on COVID-19 DPID and the experiment results indicate that our proposed metric supplies a satisfactory correlation with subjective evaluation scores, demonstrating effectiveness in the quantitative evaluation for COVID-19 disease progression.
模糊是新型冠状病毒肺炎（COVID-19）计算机断层扫描（CT）图像表现感知中的一个关键属性。通常，模糊会导致边缘扩展，从而使感染区域的形状发生变化。切比雪夫矩（TM）在形状表示方面已得到有效验证。直观地讲，同一患者在治疗过程中随时间的疾病进展表现为感染区域不同的模糊程度，由于不同的模糊程度会导致感染区域图像上TM的大小发生变化，因此可以通过TM捕捉感染区域的模糊情况。基于上述观察结果，提出了一种基于TM的COVID-19疾病进展纵向客观定量评估方法。构建了COVID-19疾病进展CT图像数据库（COVID-19 DPID），采用放射科医生的主观评分和手动轮廓绘制，可对同一患者随时间获取的CT图像上的疾病进展进行测试和比较。然后对图像进行预处理，包括肺自动分割、纵向配准、切片融合，得到带有感兴趣区域（ROI）的融合切片图像。接下来，计算融合ROI图像的梯度以表示形状。将融合ROI的梯度图像分割成相同大小的块，计算块能量作为非直流矩值的平方和。最后，通过应用块方差对TM能量进行归一化得到客观评估分数。我们在COVID-19 DPID上进行了实验，实验结果表明，我们提出的指标与主观评估分数具有良好的相关性，证明了其在COVID-19疾病进展定量评估中的有效性。
