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Accurate segmentation of medical images poses one of the major challenges in computer vision.   Approaches that rely solely on intensity information frequently fail because similar intensity values appear in multiple structures. This paper presents a method for using shape knowledge to guide the segmentation process, applying it to the task of finding the surface of the brain. A 3-D model that includes local shape constraints is fitted to an MR volume dataset. The resulting low-resolution surface is used to mask out regions far from the cortical surface, enabling an isosurface extraction algorithm to isolate a more detailed surface boundary.   The surfaces generated by this technique are comparable to those achieved by other methods, without requiring user adjustment of a large number of ad hoc parameters.