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Improving polyp detection algorithms for CT colonography: Pareto front approach

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Title: Improving polyp detection algorithms for CT colonography: Pareto front approach
Authors: Huang, Adam1,2, Li, Jiang1,3, Summers, Ronald M.1, Petrick, Nicholas4, Hara, Amy K.5
Source: Pattern Recognition Letters. Aug2010, Vol. 31 Issue 11, p1461-1469. 9p.
Subjects: POLYP (Computer system), Algorithms, Virtual colonoscopy, Performance evaluation, Pareto optimum, Evolutionary computation, Sensitivity analysis
Abstract: Abstract: We investigated a Pareto front approach to improve polyp detection algorithms for CT colonography (CTC). A dataset of 56 CTC colon surfaces with 87 proven positive detections of 53 polyps sized 4–60mm was used to evaluate the performance of a one-step and a two-step curvature-based region growing algorithm. The algorithmic performance was statistically evaluated and compared based on the Pareto optimal solutions from 20 experiments by evolutionary algorithms. The false positive rate was lower (p <0.05) by the two-step algorithm than by the one-step for 63% of all possible operating points. While operating at a suitable sensitivity level such as 90.8% (79/87) or 88.5% (77/87), the false positive rate was reduced by 24.4% (95% confidence intervals 17.9–31.0%) or 45.8% (95% confidence intervals 40.1–51.0%), respectively. We demonstrated that, with a proper experimental design, the Pareto optimization process can effectively help in fine-tuning and redesigning polyp detection algorithms. [Copyright &y& Elsevier]
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Database: Engineering Source
Description
Abstract:Abstract: We investigated a Pareto front approach to improve polyp detection algorithms for CT colonography (CTC). A dataset of 56 CTC colon surfaces with 87 proven positive detections of 53 polyps sized 4–60mm was used to evaluate the performance of a one-step and a two-step curvature-based region growing algorithm. The algorithmic performance was statistically evaluated and compared based on the Pareto optimal solutions from 20 experiments by evolutionary algorithms. The false positive rate was lower (p <0.05) by the two-step algorithm than by the one-step for 63% of all possible operating points. While operating at a suitable sensitivity level such as 90.8% (79/87) or 88.5% (77/87), the false positive rate was reduced by 24.4% (95% confidence intervals 17.9–31.0%) or 45.8% (95% confidence intervals 40.1–51.0%), respectively. We demonstrated that, with a proper experimental design, the Pareto optimization process can effectively help in fine-tuning and redesigning polyp detection algorithms. [Copyright &y& Elsevier]
ISSN:01678655
DOI:10.1016/j.patrec.2010.03.013