An Improved Lung Parenchyma Segmentation Using the Maximum Inter-Class Variance Method (OTSU)

Abstract

In lung cancer computer-aided diagnosis (CAD), the correct segmentation of the lung parenchyma is particularly important. In order to reduce the detection area, save computational time and improve accuracy, lung tissue needs to be extracted in advance. An improved method of maximum inter-class variance (OTSU) combined with morphological operations is proposed. First, the original CT image is preprocessed by filtering, denoising, image enhancement, and adaptive threshold binarization; then the connecting area marker obtains the outline, using OTSU-based improvement algorithm to remove interference such as trachea lung fluid, separates the lung essence and background, uses the column scanning, regional color marking and effectively separate the left and right lung leaf adhesion and finally uses a series of morphological operations to repair the extracted lung essence. 830 CT images were selected from the public database LIDC, and were successfully segmented using this proposed method, with an average accuracy of 97.56 percent, an average recall rate that reaches 99.29 percent, and a Dice similarity coefficient of 98.42 percent.