Rough Set theory is a new mathematical tool to deal with problems on vagueness and uncertainty. An image segmentation method based on Rough Set theory and K-means clustering is presented. The original image is segmented according to the relation of equal value. By applying value reduct to the attribute values, different regions are classified based on indiscernibility. The experimental results indicate that the method can improve veracity and stability of image segmentation.