Nonlinear Multiscale Representations for Image Segmentation

摘要

In order to segment an image the use of information at multiple scales is invaluable. The hyperstack, a linking-model-based segmentation technique, uses intensity to link points in adjacent levels of a scale space stack. This approach has been successfully applied to linear multiscale representations. Multiscale representions which satisfy two scale space properties, viz. a causality criterion and a semigroup property in differential form, are valid inputs as well. In this paper we consider linear scale space, gradient-dependent diffusion, and the Euclidean shortening flow. Since no global scale parameter is available in the latter two approaches we compare scale levels based on evolution time, information theoretic measures, and by counting the number of objects. The multiscale representations are compared with respect to their performance in image segmentation tasks on test and MR images. The hyperstack proves to be rather insensitive to the underlying multiscale representation although the nonlinear representations reduced the number of post processing steps.