Complementary texture and intensity gradient estimation and fusion for watershed segmentation

In this paper, we identify two current challenges associated with watershed segmentation algorithms which attempt to fuse the visual cues of texture and intensity. The first challenge is that most existing techniques use a competing gradient set which does not allow boundaries to be defined in terms of both visual cues. The second challenge is that these techniques fail to account for the spatial uncertainty inherent in texture gradients. We present a watershed segmentation algorithm which provides a suitable solution to both these challenges and minimises the spatial uncertainty in boundary localisation. This is achieved by a novel fusion algorithm which uses morphological dilation to integrate intensity and texture gradients.Aquantitative and qualitative evaluation of results is provided demonstrating that our algorithm outperforms three existing watershed algorithms