Katsuragawa S, Doi K, MacMahon H, Monnier-Cholley L, Morishita J, Ishida T
Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, University of Chicago, Illinois 60637, USA.
J Digit Imaging. 1996 Aug;9(3):137-44. doi: 10.1007/BF03168609.
We are developing a computerized method for detection and characterization of interstitial diseases based on a quantitative analysis of geometric features of various infiltrate patterns in digital chest radiographs. In our approach, regions of interest (ROIs) with 128 x 128 matrix size (22.4 mm x 22.4 mm) are automatically selected, covering peripheral lung regions. Next, nodular and linear opacities, which are the basic components of interstitial infiltrates, are identified from two processed images obtained by use of a multiple-level thresholding technique and a line enhancement filter, respectively. Finally, the total area of nodular opacities and the total length of linear opacities in each ROI are determined as measures of geometric pattern features. We have applied this computer analysis to 72 ROIs with normal and abnormal patterns that were classified in advance by six chest radiologists. Preliminary results indicate that the distribution of measures of geometric-pattern features correlate well with radiologists' classification. These early results are encouraging, and further evaluation hopes to establish that this computerized method might prove useful to radiologists in their assessment of interstitial diseases.
我们正在开发一种基于对数字化胸部X光片中各种浸润模式的几何特征进行定量分析来检测和表征间质性疾病的计算机化方法。在我们的方法中，自动选择大小为128×128矩阵（22.4毫米×22.4毫米）的感兴趣区域（ROI），覆盖肺外周区域。接下来，分别从通过多级阈值技术和线增强滤波器获得的两幅处理图像中识别出作为间质性浸润基本组成部分的结节状和线性不透明区域。最后，确定每个ROI中结节状不透明区域的总面积和线性不透明区域的总长度，作为几何模式特征的度量。我们已将这种计算机分析应用于72个具有正常和异常模式的ROI，这些ROI事先由六位胸部放射科医生进行了分类。初步结果表明，几何模式特征度量的分布与放射科医生的分类相关性良好。这些早期结果令人鼓舞，进一步评估希望证实这种计算机化方法可能对放射科医生评估间质性疾病有用。
