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 language | English (US) |
---|---|
Pages (from-to) | 661-675 |
Number of pages | 15 |
Journal | IEEE Transactions on Medical Imaging |
Volume | 23 |
Issue number | 6 |
DOIs | |
State | Published - Jun 2004 |
Externally published | Yes |
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