A Review of Intelligent Image Processing Method of Pulmonary CT Images Due to the high degree of air pollution, high smoking rate and poor production protection conditions during the industrialization process, the incidence of pulmonary diseases such as pulmonary cancer and chronic obstructive pulmonary disease has been maintained at a high level in the world and has shown an increasing trend, which has become one of the diseases with the highest mortality rate. In recent years, with the rapid development of artificial intelligence technology, the intelligent assisted diagnosis and surgery technology of pulmonary CT images has made great progress, and played an important role in the early diagnosis and precise treatment of pulmonary diseases. This paper mainly discusses the development history of imageassisted diagnosis and surgery technology for pulmonary diseases, as well as the method of segmentation and evaluation of pulmonary tissue and lesion, and the clinical application of pulmonary intelligent assisted diagnosis and surgery systems.

A Segmentation Method of Lung Parenchyma From Chest CT Images Based on Dual UNet Segmentation of lung parenchyma as a pretreatment step for the detection of some lung lesions requires high accuracy to provide more reliable information for lesion detection. In this paper, we proposed a segmentation method of lung parenchyma from chest CT Images by Dual UNet algorithm based on the characteristics of different lung region. Firstly, the original lung region is extracted from the full chest CT images of patient by an automatic threshold selection method. Then, the images of the different regions of lung is obtained by calculating the area. Finally, the Dual UNet is trained according the different lung regions. The first UNet algorithm operates these middle chest images and other images are operated by the second UNet algorithm to reduce the interference of other images. The experimental results shown that the segmenting results of lung parenchyma by this method are more accurate than the results by single UNet and UNet algorithm.