Carreira M J, Cabello D, Penedo M G, Mosquera A
Department of Electronics and Computer Science, University of Santiago de Compostela, Spain.
Med Phys. 1998 Oct;25(10):1998-2006. doi: 10.1118/1.598388.
This work describes a computational scheme for automatic detection of suspected lung nodules in a chest radiograph. A knowledge-based system extracts the lung masks over which we will apply the nodule detection process. First we obtain the normalized cross-correlation image. Next we detect suspicious regions by assuming a threshold. We examine the suspicious regions using a variable threshold which results in the growth of the suspicious areas and an increase in false positives. We reduce the large number of false positives by applying the facet model to the suspicious regions of the image. An algorithmic classification process gives a confidence factor that a suspicious region is a nodule. Five chest images containing 30 known nodules were used as a training set. We evaluated the system by analyzing 30 chest images with 40 confirmed nodules of varying contrast and size located in various parts of the lungs. The system detected 100% of the nodules with a mean of six false positives per image. The accuracy and specificity were 96%.
这项工作描述了一种用于在胸部X光片中自动检测疑似肺结节的计算方案。一个基于知识的系统提取肺部掩码，我们将在其上应用结节检测过程。首先，我们获得归一化互相关图像。接下来，我们通过假设一个阈值来检测可疑区域。我们使用可变阈值检查可疑区域，这会导致可疑区域扩大和误报增加。我们通过将小面模型应用于图像的可疑区域来减少大量误报。一个算法分类过程给出一个可疑区域是结节的置信度因子。五张包含30个已知结节的胸部图像用作训练集。我们通过分析30张胸部图像来评估该系统，这些图像中有40个已确认的结节，对比度和大小各异，分布在肺部的不同部位。该系统检测到了100%的结节，每张图像平均有六个误报。准确率和特异性为96%。
