A user-guided tool for efficient segmentation of medical image data

The lack of robust and reproducible methods for object segmentation still impedes the introduction of image postprocessing as widely used routine tools in clinical environments. In this paper, we present new tools for the segmentation of two- and three-dimensional objects from multidimensional image data. Our strategy is twofold: After creating an extended graph description of contour fragments and a tessellation of the image plane which is a fully automatic process running in the background, a user can choose between an interactive and a model-based segmentation procedure. A contour grouping algorithm based on path optimization can be used when full user interaction is required. Interactivity is limited to a few simple and quick operations. Another, region-based method uses a split-and-merge strategy and discrete optimization with global shape criteria. Grouping of primitive region patches is invoked by a contour model and a comparison of shape features. In combination, the two procedures form an efficient slice-propagation technique for the segmentation of volumetric objects from three-dimensional image data.