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Automated detection of lung nodules in CT scans: False‐positive reduction with the radial‐gradient index
Author(s) -
Roy Arunabha S.,
Armato Samuel G.,
Wilson Andrew,
Drukker Karen
Publication year - 2006
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.2178450
Subject(s) - false positive paradox , false positive rate , linear discriminant analysis , artificial intelligence , pattern recognition (psychology) , radiology , mathematics , nuclear medicine , computer science , medicine
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.