Abstract:
The correct segmentation of lung parenchyma is very important in the computer-aided diagnosis (CAD) of lung diseases. In order to reduce detection area, save operation time and improve accuracy, it is necessary to extract lung tissue in advance. In this paper, an improved method of lung parenchyma segmentation based on the maximum inter-class variance method (OTSU) and morphological operation is proposed. First, the original CT image is filtered and denoised, image enhancement preprocessing, adaptive threshold binarization image, and then connected area label to obtain contour, then the improved algorithm based on OTSU is used to remove the trachea, lung fluid and other interference. The lung parenchyma and background were separated, The left and right pulmonary leaf adhesion are marked with color and separated effectively by row and column scan. Finally, a series of morphological operations are used to repair the extracted lung parenchyma. To select 830 CT images from the open database LIDC, and the lung parenchyma could be segmented completely by this method. The average accuracy is 97.56%, and the average recall rate is as high as 99.29%, Dice similarity coefficient is 98.42%.