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Square-cut: A Segmentation Algorithm on the Basis of a Rectangle Shape

1Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
2Department of Neurosurgery, University of Marburg, Marburg, Germany.
3Department of Mathematics and Computer Science, University of Marburg, Marburg, Germany.
4Computer Graphics Group, University of Siegen, Siegen, Germany.
Public Library of Science
Publication Date:
PLoS One
Volume Number:
Issue Number:
PLoS One. 2012 Feb;7(2):e31064.
PubMed ID:
Appears in Collections:
P41 RR019703/RR/NCRR NIH HHS/United States
R03 EB013792/EB/NIBIB NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Egger J., Kapur T., Dukatz T., Kolodziej M., Zukić D., Freisleben B., Nimsky C. Square-cut: A Segmentation Algorithm on the Basis of a Rectangle Shape. PLoS One. 2012 Feb;7(2):e31064. PMID: 22363547. PMCID: PMC3283589.
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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 non-uniformly 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±2.2%.

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