A unifying graph-cut image segmentation framework: algorithms it encompasses and equivalences among them


Krzysztof Chris Ciesielski, J.K. Udupa, A.X. Falcao, and P.A.V. Miranda

Medical Imaging 2012: Image Processing, SPIE Proceedings 8314, 2012.

We present a general graph-cut segmentation framework GGC, in which the delineated objects returned by the algorithms optimize the energy functions associated with the lp norm, p in [1,\infty]. Two classes of well known algorithms belong to GGC: the standard graph cut GC (such as the min-cut/max-flow algorithm) and the relative fuzzy connectedness algorithms RFC (including iterative RFC, IRFC). The norm-based description of GGC provides more elegant and mathematically better recognized framework of our earlier results. Moreover, it allows precise theoretical comparison of GGC representable algorithms with the algorithms discussed in a recent paper on Power Watershed (min-cut/max-flow graph cut, random walker, shortest path/geodesic, Voronoi diagram, power watershed/shortest path forest), which optimize, via lp norms, the intermediate segmentation step, the labeling of scene voxels, but for which the final object need not optimize the used lp energy function.

Conference Proceeding reprint.

Full paper.

Last modified March 25, 2012.