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

Krzysztof Chris Ciesielski; Jayaram K. Udupa; A. X. Falcão; P. A. V. Miranda

doi:10.1117/12.911810

14 February 2012 A unifying graph-cut image segmentation framework: algorithms it encompasses and equivalences among them

Krzysztof Chris Ciesielski, Jayaram K. Udupa, A. X. Falcão, P. A. V. Miranda

Author Affiliations +

Proceedings Volume 8314, Medical Imaging 2012: Image Processing; 83143C (2012) https://doi.org/10.1117/12.911810
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

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 ℓ_p 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 ℓ_p norms, the intermediate segmentation step, the labeling of scene voxels, but for which the final object need not optimize the used ℓ_p 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.

Citation Download Citation

Krzysztof Chris Ciesielski, Jayaram K. Udupa, A. X. Falcão, and P. A. V. Miranda "A unifying graph-cut image segmentation framework: algorithms it encompasses and equivalences among them", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83143C (14 February 2012); https://doi.org/10.1117/12.911810

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE