Blurring strategies for image segmentation using a multiscale linking model

Multiscale approaches are an invaluable tool for image segmentation. A vast amount of research has been devoted to the construction of different multiscale representations of an image. In this paper we use the hyperstack-a multiscale linking model for image segmentation-for an in-depth comparison of four different scale space generators with respect to segmentation results. We consider the linear (Gaussian) scale space both in the spatial and the Fourier domain, the variable conductance diffusion according to the Perona and Malik equation, and the Euclidean shortening flow. We have done experiments on MR images of the brain, for which a gold standard is available. The hyperstack proves to be rather insensitive to the underlying scale space generator.