Automatic detection of lung nodules is an important problem in computer analysis of chest radiographs. In this paper, we propose\r\na novel algorithm for isolating lung abnormalities (nodules) fromspiral chest low-dose CT (LDCT) scans.The proposed algorithm\r\nconsists of three main steps. The first step isolates the lung nodules, arteries, veins, bronchi, and bronchioles from the surrounding\r\nanatomical structures. The second step detects lung nodules using deformable 3D and 2D templates describing typical geometry\r\nand gray-level distribution within the nodules of the same type. The detection combines the normalized cross-correlation template\r\nmatching and a genetic optimization algorithm. The final step eliminates the false positive nodules (FPNs) using three features that\r\nrobustly define the true lung nodules. Experiments with 200 CT data sets show that the proposed approach provided comparable\r\nresults with respect to the experts.
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