A novel approach of computer-aided detection of focal ground-glass opacity in 2D lung CT image

Focal Ground-Glass Opacity (fGGO) plays an important role in diagnose of lung cancers. This paper proposes a novel approach for detecting fGGOs in 2D lung CT images. The approach consists of two stages: extracting regions of interests (ROIs) and labeling each ROI as fGGO or non-fGGO. In the first stage, we use the techniques of Otsu thresholding and mathematical morphology to segment lung parenchyma from lung CT images and extract ROIs in lung parenchyma. In the second stage, a Bayesian classifier is constructed based on the Gaussian mixture Modeling (GMM) of the distribution of visual features of fGGOs to fulfill ROI identification. The parameters in the classifier are estimated from training data by the discriminative learning method of Max-Min posterior Pseudo-probabilities (MMP). A genetic algorithm is further developed to select compact and discriminative features for the classifier. We evaluated the proposed fGGO detection approach through 5-fold cross-validation experiments on a set of 69 lung CT scans that contain 70 fGGOs. The proposed approach achieves the detection sensitivity of 85.7% at the false positive rate of 2.5 per scan, which proves its effectiveness. We also demonstrate the usefulness of our genetic algorithm based feature selection method and MMP discriminative learning method through comparing them with without-selection strategy and Support Vector Machines (SVMs), respectively, in the experiments.