Sakai N, Mishima M, Nishimura K, Itoh H, Kuno K
Department of Clinical Physiology, Kyoto University, Japan.
Chest. 1994 Nov;106(5):1319-25. doi: 10.1378/chest.106.5.1319.
We developed an automated method to recognize the lung in a computed tomographic (CT) image. With computer-assisted analysis, we were able to describe the continuous low attenuation (less than -960 Hounsfield units) areas (CLA) on chest CT scans. The size (CLAs) and number (CLAn) of the CLA and the percentage of total lung area occupied by low attenuation area (LAA%) were measured using CT scans obtained from 24 patients with chronic pulmonary emphysema (CPE) and 13 control patients. The automated algorithm recognized the lung areas successfully in all patients. The CLAs and LAA% were significantly higher, and CLAn was significantly lower in patients with CPE than in controls. There was a significant correlation between CT parameters and pulmonary function test results. The histograms of the size of CLA could be represented as a power function in each patient. This automated method should be useful in objectively defining the affected areas in the lungs of patients with CPE.
我们开发了一种在计算机断层扫描（CT）图像中识别肺部的自动化方法。通过计算机辅助分析，我们能够描述胸部CT扫描上的连续低衰减（小于-960亨氏单位）区域（CLA）。使用从24例慢性肺气肿（CPE）患者和13例对照患者获得的CT扫描测量CLA的大小（CLAs）和数量（CLAn）以及低衰减区域占总肺面积的百分比（LAA%）。该自动化算法在所有患者中均成功识别出肺部区域。CPE患者的CLAs和LAA%显著更高，而CLAn显著低于对照组。CT参数与肺功能测试结果之间存在显著相关性。CLA大小的直方图在每位患者中都可表示为幂函数。这种自动化方法应有助于客观地界定CPE患者肺部的受累区域。
