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Medical image segmentation via min s-t cuts with side constraints Jiun-Hung Chen and Linda G. Shapiro
 

Summary: Medical image segmentation via min s-t cuts with side constraints
Jiun-Hung Chen and Linda G. Shapiro
Computer Science and Engineering
University of Washington, Seattle, WA 98195
{jhchen,shapiro}@cs.washington.edu
Abstract
Graph cut algorithms (i.e., min s-t cuts) [3][10][15]
are useful in many computer vision applications. In this
paper we develop a formulation that allows the addi-
tion of side constraints to the min s-t cuts algorithm in
order to improve its performance. We apply this formu-
lation to foreground/background segmentation and pro-
vide empirical evidence to support its usefulness. From
our experiments on medical image segmentation, the
graph cut with constraints achieve significantly better
performance than that without any constraint. Although
the constrained min s-t cut problem is generally NP-
hard, our approximation algorithm that uses linear pro-
gramming relaxation and a simple rounding technique
as a heuristic produces good results in a few seconds

  

Source: Anderson, Richard - Department of Computer Science and Engineering, University of Washington at Seattle

 

Collections: Computer Technologies and Information Sciences