Surface normal overlap: A computer-aided detection algorithm with application to colonic polyps and lung nodules in helical CT

David S. Paik, Christopher F. Beaulieu, Geoffrey D. Rubin, Burak Acar, R. Brooke Jeffrey, Judy Yee, Joyoni Dey, Sandy Napel

Research output: Contribution to journalArticle

214 Scopus citations

Abstract

We developed a novel computer-aided detection (CAD) algorithm called the surface normal overlap method that we applied to colonic polyp detection and lung nodule detection in helical computed tomography (CT) images. We demonstrate some of the theoretical aspects of this algorithm using a statistical shape model. The algorithm was then optimized on simulated CT data and evaluated using a per-lesion cross-validation on 8 CT colonography datasets and on 8 chest CT datasets. It is able to achieve 100% sensitivity for colonic polyps 10 mm and larger at 7.0 false positives (FPs)/dataset and 90% sensitivity for solid lung nodules 6 mm and larger at 5.6 FP/dataset.

Original languageEnglish (US)
Pages (from-to)661-675
Number of pages15
JournalIEEE Transactions on Medical Imaging
Volume23
Issue number6
DOIs
StatePublished - Jun 1 2004
Externally publishedYes

Keywords

  • Colonic polyp
  • Computed tomography colonography (CTC)
  • Computer-aided detection (CAD)
  • Cross-validation
  • Lung nodule
  • Statistical shape model

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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