ABSTRACT We examine the use of three techniques, graph cuts, isoperimetric minimization and random-walk partitioning for the interactive segmentation of cardiovascular medical images. These methods can often be used effectively without heavy reliance on learned or explicitly encoded priors. We illustrate, through the use of a toy problem, the basic difference in the performance characteristics of the methods. Subsequently, the suitability of each method to a particular segmentation application in the cardiovascular imaging domain is demonstrated.
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