TY - JOUR
T1 - Surface normal overlap
T2 - A computer-aided detection algorithm with application to colonic polyps and lung nodules in helical CT
AU - Paik, David S.
AU - Beaulieu, Christopher F.
AU - Rubin, Geoffrey D.
AU - Acar, Burak
AU - Jeffrey, R. Brooke
AU - Yee, Judy
AU - Dey, Joyoni
AU - Napel, Sandy
N1 - Funding Information:
Manuscript received September 22, 2003; revised February 5, 2004. This work was supported in part by the National Institutes of Health (NIH) under Grant R01-CA72023 and Grant P41-RR09784. The Associate Editor responsible for coordinating the review of this paper and recommending its publication was G. Wang. Asterisk indicates corresponding author. *D. S. Paik is with the Department of Radiology, Stanford University, Stanford, CA, USA 94305-5450 USA (e-mail: [email protected]).
PY - 2004/6
Y1 - 2004/6
N2 - 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.
AB - 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.
KW - Colonic polyp
KW - Computed tomography colonography (CTC)
KW - Computer-aided detection (CAD)
KW - Cross-validation
KW - Lung nodule
KW - Statistical shape model
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U2 - 10.1109/TMI.2004.826362
DO - 10.1109/TMI.2004.826362
M3 - Article
C2 - 15191141
AN - SCOPUS:2942577813
SN - 0278-0062
VL - 23
SP - 661
EP - 675
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
IS - 6
ER -