Accurate segmentation of lung parenchyma in lung CT images is a key step in the detection and diagnosis of lung diseases. In view of the fact that the traditional image segmentation method is not ideal for lung parenchyma segmentation in CT images, a lung parenchyma segmentation method based on optimal threshold method and improved Freeman chain code is proposed. Firstly, the optimal threshold method is used to realize the initial segmentation of lung parenchyma, then the lung parenchyma template is further processed. Secondly, the defective template is repaired by the improved Freeman chain code method and Bezier curve. Finally, the lung parenchyma is extracted by its multiplication with CT images of the lung. The accuracy of lung parenchyma segmentation is improved in image contrast clarity and consistency of lung parenchyma features, and the average segmentation accuracy can reach 96.8%. The experimental results show that, for marginal nodules and different lung lesions, this method has ideal segmentation effect and has good accuracy and robustness.