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Proceedings Paper

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

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, 1 ≤ p ≤ ∞. 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 from [18, 19]. Moreover, it allows precise theoretical comparison of GGC representable algorithms with the algorithms discussed in a recent paper [22] (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. Actually, the comparison of the GGC representable algorithms with that encompassed in the framework described in [22] constitutes the main contribution of this work.

Paper Details

Date Published: 14 February 2012
PDF: 12 pages
Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83143C (14 February 2012); doi: 10.1117/12.911810
Show Author Affiliations
Krzysztof Chris Ciesielski, West Virginia Univ. (United States)
The Univ. of Pennsylvania (United States)
Jayaram K. Udupa, The Univ. of Pennsylvania Health System (United States)
A. X. Falcão, Univ. of Campinas (Brazil)
P. A. V. Miranda, Univ. of São Paulo (Brazil)


Published in SPIE Proceedings Vol. 8314:
Medical Imaging 2012: Image Processing
David R. Haynor; Sébastien Ourselin, Editor(s)

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