Image thresholding is a common technique used in computer vision to analyze and visualize images. The Otsu method is a popular thresholding technique, but its performance under different conditions has not been thoroughly studied. A recent analysis found that successful image segmentation using the Otsu technique depends on the intensity difference between objects and the background, object size, and noise levels. The location of the object in the image does not affect the segmentation outcome. By establishing specific conditions, researchers were able to accurately predict the success of image thresholding using the Otsu technique. This study provides valuable insights for improving image analysis algorithms.