Oriented boundary graph: An efficient structuring model for segmentation of 3D images

From a theoretical point of view, most of image segmentation methods that have been developed for 2D images can be generalized to higher dimensions. In actual practice, the cost in space to encode 3D data structure and the cost in time to run 3D algorithms does not allow to conveniently implement those classical segmentation algorithms in the 3D case. In this article, we describe a new model to efficiently represent and update both the topological and the geometrical structure of the regions of a 3D segmented image. This model has been defined from a pragmatical approach that consists in specifying a basic region-based segmentation framework, and then in building a minimal model that encodes all the relationships needed for an efficient implementation of this framework. This approach leads to a model suitable for a wide range of segmentation methods and allowing an efficient computation of most of the segmentation criteria involved in image segmentation.