  We present a rectangle-based segmentation algorithm that sets up a graph and
performs a graph cut to separate an object from the background. However,
graph-based algorithms distribute the graph's nodes uniformly and equidistantly
on the image. Then, a smoothness term is added to force the cut to prefer a
particular shape. This strategy does not allow the cut to prefer a certain
structure, especially when areas of the object are indistinguishable from the
background. We solve this problem by referring to a rectangle shape of the
object when sampling the graph nodes, i.e., the nodes are distributed
nonuniformly and non-equidistantly on the image. This strategy can be useful,
when areas of the object are indistinguishable from the background. For
evaluation, we focus on vertebrae images from Magnetic Resonance Imaging (MRI)
datasets to support the time consuming manual slice-by-slice segmentation
performed by physicians. The ground truth of the vertebrae boundaries were
manually extracted by two clinical experts (neurological surgeons) with several
years of experience in spine surgery and afterwards compared with the automatic
segmentation results of the proposed scheme yielding an average Dice Similarity
Coefficient (DSC) of 90.97\pm62.2%.
