Volume-based feature analysis of mucosa for automatic initial polyp detection in virtual colonoscopy

Purpose: Purpose A volume-based mucosa-based polyp candidate determination scheme for automatic polyp detection in computed colonography is presented in this paper. Methods: Different from the one-layer mucosa that is widely accepted by the existing computer-aided detection methods, a thick mucosa region of 3-5 voxels wide is extracted, which excludes the direct applications of the traditional geometrical features. A fast marching-based adaptive gradient/curvature and weighted integral curvature along normal directions (WICND) are developed for this purpose, and polyp candidates are optimally determined by computing and clustering these fast marching-based adaptive geometrical features. Results: By testing on 52 patients datasets in which 26 patients were found with polyps of size 4-22 mm, both the locations and number of polyp candidates detected by WICND and previously developed linear integral curvature (LIC) were compared. Not only the number of false positives (FPs) was reduced from 706 to 132 on average, but also the detection sensitivity has been slightly improved. Conclusions: WICND outperformed LIC mainly by significantly reducing the number of FPs, which promises to release our burden of machine learning in the feature space, especially for those polyps smaller than 5 mm.