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Candidate generation, the first stage for most computer aided detection (CAD)systems, rapidly scans the entire image data for any possible abnormalitylocations, while the subsequent stages of the CAD system refine the candidateslist to determine the most probable or significant of these candidates. Thecandidate generator creates a list of the locations and provides a sizeestimate for each candidate. A multiscale scale-normalized Laplacian ofGaussian (LoG) filtering method for detecting pulmonary nodules in whole-lungCT scans, presented in this paper, achieves a high sensitivity for both solidand nonsolid pulmonary nodules. The pulmonary nodule LoG filtering method wasvalidated on a size-enriched database of 706 whole-lung low-dose CT scanscontaining 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 asensitivity of 1.000 (107/107) for nonsolid nodules. Furthermore, compared toradiologist measurements, the method provided low average nodule sizeestimation error of 0.12 mm for solid and 1.27 mm for nonsolid nodules. Theaverage distance between automatically and manually determined nodule centroidswere 1.41 mm and 1.43 mm, respectively.

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