Nonlinear Diffusion Filters without Parameters for Image Segmentation

Nonlinear diffusion filtering seeks to improve images qualitatively by removing noise while preserving details and even enhancing edges. However, well known implementations are sensitive to parameters which are necessarily tuned to sharpen a narrow range of edge slopes. In this work, we have selected a nonlinear diffusion filter without control parameters. It has been guided searching the optimum balance between time performance and resulting quality suitable for automatic segmentation tasks. Using a semi-implicit numerical scheme, we have determined the relationship between the slope range to sharpen and the diffusion time. It has also been selected the diffusivity with optimum performances. Several diffusion filters have been applied to noisy computed tomography images and evaluated for their suitability to the medical image segmentation. Experimental results show that our proposal of filter performs quite well in relation to others.