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Detection and recognition of lung nodules in spiral CT images using deformable templates and Bayesian post-classification

In this paper, we propose a novel algorithm for isolating lung abnormalities (nodules) from low dose spiral chest CT scans. The proposed algorithm consists of three main steps. The first step isolates the lung nodules, arteries, veins, bronchi, and bronchioles from the surrounding anatomical structures. The second step detects lung nodules using deformable 2D and 3D templates describing typical geometry and gray level distribution within the nodules of the same type. The detection combines the normalized cross-correlation template matching and genetic optimization algorithm. The final step eliminates the false positive nodules (FPNs) using three features that robustly define the true lung nodules. Accurate density estimation for these three features is obtained using logistic regression model and linear combination of Gaussians (LCG) with positive and negative components. This paper focuses on the second and third steps. Experiments with 200 patients' CT scans demonstrate the accuracy of our approach.