A novel 2D ground-glass opacity detection method through local-to-global multilevel thresholding for segmentation and minimum bayes risk learning for classification

Ground-glass opacity (GGO) detection is paramount for the prognosis and diagnosis of lung diseases. We present a novel GGO detection method for 2D lung CT images in this paper, which focuses on detecting GGOs with high sensitivity and reducing false positives as much as possible. To this end, we propose a localto- global multilevel thresholding algorithm for segmentation and a novel discriminative learning algorithm for identification to solve the problem of GGO detection. There are two components in our method. In the first component, we perform clustering on the local Ostu thresholds of CT levels for each patch of an image, the candidate regions of interests (ROIs) are segmented based on the clustering results by multilevel thresholding techniques. The second component is a Bayesian modeling process for identifying the GGOs from ROI candidates, the classifier is trained based on Bayesian risk minimization and margin maximization by our discriminative learning algorithm. The proposed GGO detection approach is evaluated on the LISS database with 45 GGOs. Finally, our detection approach performed better than other GGO detection methods in the experimental results, which achieved a sensitivity of 100% and a specificity of 33.13%.