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Traditional statistical thresholding algorithms use only class variance sum as a standard for threshold selection. These algorithms overlook characteristics of surrounding thin airways and fail to obtain without leakage. The airways are the leakage of fluid into the surrounding lung parenchyma which would result in low contrast between the airways and the lung parenchyma due to noise or pathologies. We propose, a novel criterion combining the class variance sum and discrepancy of variances in between thin airways and lung parenchyma to eliminate the described drawback for traditional statistical process. After that, we use morphological methods to identify candidate airways on CT slices and then reconstruct a connected three dimensional airway tree. Extensive validation work was done using Lung TIME database. The experimental airway test results substantial increase number of branches and total tree length. From inspiration scans on the mean number of branches detected is 46.5%, the tree length detected is 42.33% and the number of false positives is 0.28%.

Segmentation of Airways in Lung Region Using Novel Statistical Thresholding and Morphology Methods