Dynamic edge tracing: Boundary identification in medical images

Medical image segmentation is a sufficiently complex problem that no single strategy has proven to be completely effective. Historically, region growing, clustering, and edge tracing have been used and while significant steps have been made in the first two, research into automatic, recursive, boundary following has not kept pace. A new, advanced, edge-tracing algorithm capable of combining edge, region, and pixel-classification information, and suitable for magnetic resonance image analysis, is described. The algorithm is inspired by automatic target tracking, as used in civilian and military aerospace operations. Comparison with clustering and level sets is performed. Results indicate that no method is uniformly superior, that the new algorithm provides information not available from the other approaches, and that it can utilize a variety of sources including results from other methods. The algorithm is applied to two-dimensional slice images and extension to three-dimensional images is discussed.