In the field of industrial practice, the images are complicated because the conditions of imaging are usually poor and difficult to control. The image segmentation for complex imaging conditions is not easy. To solve this problem, a new threshold segmentation method is proposed based on Tsallis relative entropy and Gaussian distribution. In the method, the gray level histogram of image after segmentation is fitted by Gaussian distribution, and the difference between the histogram of original image and the fitted histogram is measured by Tsallis relative entropy. The optimal threshold is determined by minimizing the Tsallis relative entropy. Finally, the performance of the proposed method is compared with the several methods on segmentation of non-destructive testing images and synthetic aperture radar image. The results demonstrate that the proposed method has better visual effect, higher precision of segmentation, smaller segmentation error and less computational time. Thus, the proposed method has a good prospect in further applications.