Giger M L, Doi K, MacMahon H
Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, University of Chicago, Illinois 60637.
Med Phys. 1988 Mar-Apr;15(2):158-66. doi: 10.1118/1.596247.
We are investigating the characteristic features of lung nodules and the surrounding normal anatomic background in order to develop an algorithm of computer vision for use as an aid in the detection of nodules in digital chest radiographs. Our technique involves an attempt to eliminate the background anatomic structures in the lung fields by means of a difference image approach. Then, feature-extraction techniques, such as tests for circularity, size, and their variation with threshold level, are applied so that suspected nodules can be isolated. Preliminary results of this automated detection scheme yielded high true-positive rates and low false-positive rates in the peripheral lung regions of the chest. This detection scheme, which can assist the final diagnosis by the clinician, has the potential to improve the early detection of lung carcinomas.
我们正在研究肺结节的特征以及周围正常的解剖背景，以便开发一种计算机视觉算法，用于辅助检测数字胸部X光片中的结节。我们的技术涉及通过差分图像方法尝试消除肺野中的背景解剖结构。然后，应用特征提取技术，如圆形度、大小测试及其随阈值水平的变化，以便分离出可疑结节。这种自动检测方案的初步结果在胸部外周肺区域产生了高真阳性率和低假阳性率。这种检测方案可以协助临床医生进行最终诊断，具有提高肺癌早期检测率的潜力。
