In orderto improve the thresholding accuracy and adaptability of the existing OTSU thresholding method, a symmetry-constrained between-class variance thresholding method isproposed. The proposed approach initially employs the Prewitt operator to construct a gradient magnitude image from the input image, followed by the extraction of symmetric sampling areas based on the principle of symmetry. Then, a threshold is selected based on the symmetry- constrained between-class variance objective function maximization criterion, and it is judged whether the symmetric sampling areas satisfy the symmetry condition under this threshold. Ifthe symmetry condition cannotbe satisfied, the inputimage is processed with symmetry correction based on the symmetric sampling areas. The threshold is then selected using the symmetry-constrained between-class variance objective function on the rectified symmetric sampling area. Finally, thechosen thresholdisutilizedforthresholdingtheinputimage. Theperformanceoftheproposed method is compared with OTSU's method and fourimproved methods of OTSU's method on a dataset comprising28 synthetic images and 70 real-world images. Experimental results demonstrate that the proposed method achieves a misclassification errorrate of0.010 6 and 0.016 on synthetic and real-worldimages, respectively. In comparison to the second-bestmethod in terms ofthresholding accuracy, the proposed method reduces themisclassification error rates by 91.4% and 86.1% on synthetic and real-world images, respectively. Although the proposed method does not exhibit superiorityin terms of computational efficiency, it demonstrates a more robust thresholding adaptability and higher thresholding accuracy across diverse modalities oftestimages.