Improving polyp detection algorithms for CT colonography: Pareto front approach

Adam Huang, Jiang Li, Ronald M. Summers, Nicholas Petrick, Amy K. Hara

Research output: Contribution to journalArticle

7 Scopus citations

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 4-60 mm 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.

Original languageEnglish (US)
Pages (from-to)1461-1469
Number of pages9
JournalPattern Recognition Letters
Volume31
Issue number11
DOIs
StatePublished - Aug 1 2010

Keywords

  • CT colonography
  • Computer-aided detection
  • Pareto front
  • Polyp detection
  • Virtual colonoscopy

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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