A multiscale Laplacian of Gaussian (LoG) filtering approach to pulmonary nodule detection from whole-lung CT scans

Candidate generation, the first stage for most computer aided detection (CAD) systems, rapidly scans the entire image data for any possible abnormality locations, while the subsequent stages of the CAD system refine the candidates list to determine the most probable or significant of these candidates. The candidate generator creates a list of the locations and provides a size estimate for each candidate. A multiscale scale-normalized Laplacian of Gaussian (LoG) filtering method for detecting pulmonary nodules in whole-lung CT scans, presented in this paper, achieves a high sensitivity for both solid and nonsolid pulmonary nodules. The pulmonary nodule LoG filtering method was validated on a size-enriched database of 706 whole-lung low-dose CT scans containing 499 solid (>= 4 mm) and 107 nonsolid (>= 6 mm) pulmonary nodules. The method achieved a sensitivity of 0.998 (498/499) for solid nodules and a sensitivity of 1.000 (107/107) for nonsolid nodules. Furthermore, compared to radiologist measurements, the method provided low average nodule size estimation error of 0.12 mm for solid and 1.27 mm for nonsolid nodules. The average distance between automatically and manually determined nodule centroids were 1.41 mm and 1.43 mm, respectively.