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Existing polyp detection methods rely heavily on curvature-based characteristics to differentiate between lesions. However, as curvature is a local feature and a second order differential quantity, noise caused by small bumpy structures and incoherent curvature fields of a discretized volume or surface can greatly increase the number of false positives (FPs) detected. This paper investigates a spectral compression and curvature tensor smoothing algorithm with the aim to reduce the number of FPs detected while preserving true positives. Simulation results give 96% sensitivity for polyps >10 mm while reducing FPs by 92%.