Many methods for supervised image segmentation exist. One such algorithm, Random Walks, is very fast and accurate when compared to other methods. A drawback to Random Walks is that it has difficulty producing accurate and clean segmentations in the presence of noise. Therefore, we propose an extension to Random Walks that improves its performance without significantly modifying the original algorithm. Our extension, known as “Scale-Space Random Walks”, or SSRW, addresses these problems. The SSRW is able to produce more accurate segmentations in the presence of noise while still retaining all of the properties of the original algorithm.