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

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...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2004-06, Vol.23 (6), p.661-675
Main Authors: Paik, D.S., Beaulieu, C.F., Rubin, G.D., Acar, B., Jeffrey, R.B., Yee, J., Dey, J., Napel, S.
Format: Article
Language:English
Subjects:
Algorithms
Application software
Attenuation
Cancer
Colonic polyps
Colonic Polyps - diagnostic imaging
Computed tomography
Databases, Factual
Detection algorithms
Humans
Imaging, Three-Dimensional - methods
Lungs
Pattern Recognition, Automated
Phantoms, Imaging
Radiographic Image Interpretation, Computer-Assisted - methods
Radiology
Reproducibility of Results
Retrospective Studies
Sensitivity and Specificity
Shape
Single-Blind Method
Solitary Pulmonary Nodule - diagnostic imaging
Tomography, Spiral Computed - methods
Virtual colonoscopy
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Summary: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.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2004.826362