Image segmentation is a challenging task in image processing, especially in medical applications. Researchers have developed various techniques to analyze and segment images for easier feature recognition. One common method is thresholding, which separates objects from the background by converting grayscale images into binary ones. Thresholding can be done globally (using one threshold for the entire image) or locally (using different thresholds for smaller sections). In this study, three different thresholding methods (Otsu’s, Feng’s, and Sauvola) were compared for their efficiency in image segmentation using MATLAB.