Automated detection of lung nodules in CT scans: false-positive reduction with the radial-gradient index.
Automated detection of lung nodules in CT scans: false-positive reduction with the radial-gradient index.
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CT 扫描中肺结节的自动检测：利用径向梯度指数减少假阳性。
DOI：
10.1118/1.2178450
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发表时间：
2006
期刊：
影响因子：
3.8
通讯作者：
Drukker,Karen
中科院分区：
文献类型：
--
作者：
Roy,ArunabhaS;Armato3rd,SamuelG;Wilson,Andrew;Drukker,Karen
我们提出了许多基于径向梯度指数（RGI）的方法，以实现自动CT肺结节检测中的假阳性减少。使用了38例病例的数据库，其中共包含82个肺结节。对于每个CT切片，构建一个称为“RGI图”的互补图像，以增强高圆形区域，从而提高结节和正常解剖结构之间的对比度。改变对三个RGI参数的保留，以构建灵敏地消除假阳性结构的RGI滤波器。在一致性方法中，RGI过滤消除了自动方法检测到的36%的假阳性结构，而不会丢失任何真阳性。在线性判别分类器之前使用RGI滤波器可显著提高性能，在70%灵敏度下，每个切片的假阳性率从0.5降至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的特征实现了整体性能的大幅改善，在70%的固定灵敏度下，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。
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.
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影响因子：
3.8
作者：
HUO, ZM;GIGER, ML;SCHMIDT, RA
通讯作者：
SCHMIDT, RA
影响因子：
3.8
作者：
Drukker, K;Giger, ML;Mendelson, EB
通讯作者：
Mendelson, EB
用于检测乳腺病变的超声图的计算机分析
DOI：
10.1117/12.467093
发表时间：
2002
期刊：
2023 IEEE/CVF International Conference on Computer Vision (ICCV)
影响因子：
--
作者：
K. Drukker;M. Giger;Karla Horsch;C. Vyborny
通讯作者：
C. Vyborny
影响因子：
3.8
作者：
Armato, SG;Altman, MB;Roy, AS
通讯作者：
Roy, AS
影响因子：
3.8
作者：
Armato, SG;Giger, ML;MacMahon, H
通讯作者：
MacMahon, H