Most of the existing image thresholding methods are only suitable for processing the images with a specific gray level distribution. To deal with the issue of threshold selection in different gray level distribution within a unified framework, an automatic thresholding segmentation method guided by maximizing Pearson correlation is proposed. This method first performs edge detection on the original image to generate a reference template image; then it performs contour extraction on the binary images obtained by different thresholds to generate the corresponding contour images; it finally utilizes Pearson correlation coefficient to measure the similarities between different contour images and reference template images, and the threshold corresponding to the maximal similarity is selected as the final segmentation threshold. The proposed method is compared with 3 newly proposed thresholding methods and 4 nonthresholding methods. The experimental results on 4 synthetic images and 50 realworld images with different gray level distribution show that, compared with the second best method in segmentation accuracy, the proposed method is reduced by 0140 3 and 0121 5 in terms of the average misclassification error on the synthetic images and the realworld images, respectively. The proposed method has no advantage in computational efficiency, but it has more flexible segmentation adaptability to images with different gray level distribution patterns, and can obtain segmentation result images with higher accuracy.