In MR brain images, segmentation using intensity values is severely limited owing to field inhomogeneities, susceptibility artifacts and partial volume effects. Edge based segmentation methods suffer from spurious edges and gaps in boundaries. A method is presented which combines the advantages of edge based and region based segmentation. First a multiscale image representation, is constructed which favors intratissue diffusion over inter-tissue diffusion by exploiting local contrast. Subsequently a multiscale linking model (the hyperstack) is used to group voxels into a number of segments. This facilitates segmentation of grey matter, white matter and cerebrospinal fluid with minimal user interaction. Using a supervised segmentation, technique and MR simulations of a brain phantom as validation it is shown that the errors are in the order of or smaller than reported in literature.

A method is presented to segment multidimensional images using a multiscale (hyperstack) approach with probabilistic linking. A hyperstack is a voxel-based multiscale data structure whose levels are constructed by convolving the original image with a Gaussian kernel of increasing width. Between voxels at adjacent scale levels, child-parent linkages are established according to a model-directed linkage scheme. In the resulting tree-like data structure, roots are formed to indicate the most plausible locations in scale space where segments in the original image are represented by a single voxel. The final segmentation is obtained by tracing back the linkages for all roots. The present paper deals with probabilistic (or multiparent) linking. The multiparent linkage structure is translated into a list of probabilities that are indicative of which voxels are partial volume voxels and to which extent. Probability maps are generated to visualize the progress of weak linkages in scale space when going from fine to coarser scale. It is demonstrated that probabilistic linking gives a significantly improved segmentation as compared with conventional (single-parent) linking.

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.

A validation methodology for image segmentation methods is explored that is based on quality constrained cost analysis. In this methodology a segmentation method is evaluated by the cost reduction it provides relative to the cost of a full-interactive (manual) segmentation. This cost is constrained by a quality threshold, so that less-than-perfect segmentations are allowed. In this way, segmentation methods which are entirely different in nature can be compared objectively. The validation methodology is presented in its most general form, along with an example of its application to the comparison of segmentation methods.>

In the image processing literature many methods to segment 2D and 3D images have been presented. However, relatively little effort has been spent on the validation of the results of these methods. The goal of the paper is to explore a validation methodology that is based on developing a task-directed quality norm that can be used as a constraint in cost analysis. In this methodology a segmentation method is evaluated by the cost reduction it provides relative to the cost of a full-interactive (manual) segmentation. This cost is constrained by a quality threshold, so that less-than-perfect segmentations are allowed. In this way segmentation methods can be compared, which are designed for the same task, but are different of nature.>