Segmentation schemes for knowledge-based construction of individual atlases from slice-type medical images
Jeffrey. Stanier, Isabelle Bloch, Morris Goldberg · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1993
To produce an individual atlas from a set of slice-type medical images it is necessary to segment and label the structures contained in the images. Although this problem can be solved directly in a top-down fashion the volume of data makes a direct top-down approach difficult. Instead, a data-driven segmentation can be used to produce an intermediate data structure which is more easily searched using a knowledge-driven technique. Of the many methods used for 2-D and 3-D image segmentation only some produce an output which can be directly utilized by a top-down search technique. The segmentation should allow for data abstraction so decisions can be made quickly when comparing regions. The data structure of the segmentation should also allow for easy merging and splitting of the volumes of interest (VOIs) as the search for the best match to the model is performed. Lastly, the data structure should allow for display of the 3-D structure of the atlas and the data so an efficient user interface can be built. Two segmentation schemes are presented: one which uses a region growing approach to generate VOIs and another which uses a gradient-based segmentation approach.