Nagel R H, Nishikawa R M, Papaioannou J, Doi K
Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, University of Chicago, Illinois 60637, USA.
Med Phys. 1998 Aug;25(8):1502-6. doi: 10.1118/1.598326.
Clustered microcalcifications are often the first sign of breast cancer in a mammogram. Nevertheless, all clustered microcalcifications are not found by an individual radiologist reading a mammogram. The use of a second reader may find those clusters of microcalcifications not found by the first reader, thereby improving the sensitivity of detecting clustered microcalcifications. Our laboratory has developed a computerized scheme for the detection of clustered microcalcifications, which can act like a second reader, that is undergoing clinical evaluation. This paper concerns the feature analysis stage of the computer scheme, which is designed to remove some of the false-computer detections. We have examined three methods of feature analysis, namely, rule based (the method currently used), an artificial neural network (ANN), and a combined method. In an independent database of 50 images, at a sensitivity of 83%, the average number of false positive (FP) detections per image was: 1.9 for rule-based, 1.6 for ANN, and 0.8 for the combined method. We demonstrate that the combined method performs best because each of the two stages eliminates different types of false positives.
簇状微钙化常常是乳房X光片中乳腺癌的首个迹象。然而，单个放射科医生阅读乳房X光片时并不会发现所有的簇状微钙化。由另一位阅片者进行检查可能会发现第一位阅片者未发现的那些微钙化簇，从而提高检测簇状微钙化的敏感性。我们实验室已经开发出一种用于检测簇状微钙化的计算机方案，它可以像另一位阅片者一样发挥作用，目前正在进行临床评估。本文关注该计算机方案的特征分析阶段，该阶段旨在去除一些计算机误检测。我们研究了三种特征分析方法，即基于规则的方法（目前使用的方法）、人工神经网络（ANN）和一种组合方法。在一个包含50幅图像的独立数据库中，在83%的敏感性下，每幅图像的平均假阳性（FP）检测数分别为：基于规则的方法为1.9，人工神经网络为1.6，组合方法为0.8。我们证明组合方法表现最佳，因为两个阶段中的每一个都能消除不同类型的假阳性。
