Roy Arunabha S, Armato Samuel G, Wilson Andrew, Drukker Karen
Department of Radiology, The University of Chicago, Chicago, Illinois 60637, USA.
Med Phys. 2006 Apr;33(4):1133-40. doi: 10.1118/1.2178450.
We present a number of approaches based on the radial gradient index (RGI) to achieve false-positive reduction in automated CT lung nodule detection. A database of 38 cases was used that contained a total of 82 lung nodules. For each CT section, a complementary image known as an "RGI map" was constructed to enhance regions of high circularity and thus improve the contrast between nodules and normal anatomy. Thresholds on three RGI parameters were varied to construct RGI filters that sensitively eliminated false-positive structures. In a consistency approach, RGI filtering eliminated 36% of the false-positive structures detected by the automated method without the loss of any true positives. Use of an RGI filter prior to a linear discriminant classifier yielded notable improvements in performance, with the false-positive rate at a sensitivity of 70% being reduced from 0.5 to 0.28 per section. Finally, the performance of the linear discriminant classifier was evaluated with RGI-based features. RGI-based features achieved a substantial improvement in overall performance, with a 94.8% reduction in the false-positive rate at a fixed sensitivity of 70%. These results demonstrate the potential role of RGI analysis in an automated lung nodule detection method.
我们提出了一些基于径向梯度指数（RGI）的方法，以减少自动CT肺结节检测中的假阳性。使用了一个包含38个病例的数据库，其中共有82个肺结节。对于每个CT切片，构建了一个称为“RGI图”的互补图像，以增强高圆形度区域，从而改善结节与正常解剖结构之间的对比度。改变三个RGI参数的阈值，构建RGI滤波器，以灵敏地消除假阳性结构。在一种一致性方法中，RGI滤波消除了自动方法检测到的36%的假阳性结构，而没有损失任何真阳性。在使用线性判别分类器之前使用RGI滤波器，性能有显著提高，在灵敏度为70%时，每切片的假阳性率从0.5降低到0.28。最后，使用基于RGI的特征评估线性判别分类器的性能。基于RGI的特征在整体性能上有显著提高，在固定灵敏度为70%时，假阳性率降低了94.8%。这些结果证明了RGI分析在自动肺结节检测方法中的潜在作用。
