Detection of protrusions in curved folded surfaces applied to automated polyp detection in CT colonography.
Over the past years many computer aided diagnosis (CAD) schemes have been presented for the detection of colonic polyps in CT Colonography. The vast majority of these methods (implicitly) model polyps as approximately spherical protrusions. Polyp shape and size varies greatly, however and is often far from spherical. We propose a shape and size invariant method to detect suspicious regions. The method works by locally deforming the colon surface until the second principal curvature is smaller than or equal to zero. The amount of deformation is a quantitative measure of the 'protrudeness'. The deformation field allows for the computation of various additional features to be used in supervised pattern recognition. It is shown how only a few features are needed to achieve 95% sensitivity at 10 false positives (FP) per dataset for polyps larger than 6 mm.
van Wijk
Cees
C
Quantitative Imaging Group, Delft University of Technology, The Netherlands.
van Ravesteijn
Vincent F
VF
Vos
Frank M
FM
Truyen
Roel
R
de Vries
Ayso H
AH
Stoker
Jaap
J
van Vliet
Lucas J
LJ
D000465:Algorithms; D001185:Artificial Intelligence; D003111:Colonic Polyps / Q000000981:diagnostic imaging*; D023881:Colonography, Computed Tomographic / Q000379:methods*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity