The low contrast and blurred boundary of chest X-ray images seriously affect the segmentation effect of chest X-ray images. In order to diagnose and treat lung diseases quickly and accurately with chest X-ray images, this paper presents a method of chest X-ray lung image segmentation based on interclass variance and probabilistic error. Based on the pre-processing of chest X-ray images, the method firstly uses the information of human body structure in chest X-ray images for coarse image segmentation. Then, the interclass variances and probabilistic errors between the target class and the background class are calculated respectively for the preprocessed image, and a new segmentation objective function is designed to calculate the optimal threshold after non-dimension processing the interclass variances and probabilistic errors, so as to achieve the image accurate segmentation. Finally, the segmentation results of the coarse and fine segmentation processes are combined and optimized to achieve the image segmentation based on the optimal threshold. The comparative experimental results of chest X-ray images show that The DSC and IOU indicators of the proposed method are 89.5% and 81.1% respectively, and the segmentation of lung regions by the method has good performance in completeness and accuracy. This indicates that the method is effective and feasible, and is suitable for lung image segmentation based on chest X-ray images.