When a gray-level image is affected by different factors， such as the size ratio of the target to the background， noise， or random details， its gray-level histogram exhibits peakless， unimodal， bimodal， or multimodal patterns. To deal with the issue of automatic threshold selection in these four situations within a unified framework， an automatic threshold selection method using the exponential Rényi entropy under the multi-scale product in the stationary wavelet domain is proposed. First， stationary wavelet multi-scale transformation is applied to the original gray-level image in the horizontal， vertical， and diagonal directions， and a fused image is constructed via the multi-scale multiplication of high-frequency sub-bands in each direction. Then， the fused image is sampled by the inner and outer contour image to construct a one-dimensional gray-level histogram. Finally， the exponential Rényi entropy corresponding to the constructed histogram is calculated， and the threshold corresponding to the maximum exponential Rényi entropy is taken as the final threshold. The proposed method was compared with four automatic threshold segmentation methods， two clustering segmentation methods， and two active contour segmentation methods. The experimental results for 16 synthetic images and 50 real-world images indicated that with regard to the segmentation accuracy， the proposed method outperformed the second-best method by 41.2% and 20.8% in terms of the average Matthews correlation coefficient for the synthetic and real-world images， respectively. Although the proposed method has no advantage with regard to computational efficiency， it has more robust segmentation adaptability and a higher segmentation accuracy than the other eight segmentation methods.