A technique of computer grains segmentation on uranium dioxide thin section images is described. An algorithm of image preprocessing is developed, allowing to achieve more accurate results in grains boundary detection when using the watersheds marker method. The usage of grey-level morphological operations instead of binary results in information losses reduction on image processing. The developed algorithm can be used for quality control processes automation in metallurgy industrial organizations.