Surgical Planning Laboratory - Brigham & Women's Hospital - Boston, Massachusetts USA - a teaching affiliate of Harvard Medical School

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Manual Refinement System for Graph-Based Segmentation Results in the Medical Domain

Institution:
1Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. egger@bwh.harvard.edu.
2Department of Mathematics and Computer Science, University of Marburg, Marburg, Germany.
3Department of Neurosurgery, University of Marburg, Marburg, Germany.
Publisher:
Springer
Publication Date:
Oct-2012
Journal:
J Med Syst
Volume Number:
36
Issue Number:
5
Pages:
2829-39
Citation:
J Med Syst. 2012 Oct;36(5):2829-39.
PubMed ID:
21826501
PMCID:
PMC3691109
Keywords:
Segmentation, Manual refinement, 2D, 3D, Graph-based
Appears in Collections:
NCIGT, SLICER, SPL
Sponsors:
P41 RR019703/RR/NCRR NIH HHS/United States
R25 CA089017/CA/NCI NIH HHS/United States
Generated Citation:
Egger J., Colen R.R., Freisleben B., Nimsky C. Manual Refinement System for Graph-Based Segmentation Results in the Medical Domain. J Med Syst. 2012 Oct;36(5):2829-39. PMID: 21826501. PMCID: PMC3691109.
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The basic principle of graph-based approaches for image segmentation is to interpret an image as a graph, where the nodes of the graph represent 2D pixels or 3D voxels of the image. The weighted edges of the graph are obtained by intensity differences in the image. Once the graph is constructed, the minimal cost closed set on the graph can be computed via a polynomial time s-t cut, dividing the graph into two parts: the object and the background. However, no segmentation method provides perfect results, so additional manual editing is required, especially in the sensitive field of medical image processing. In this study, we present a manual refinement method that takes advantage of the basic design of graph-based image segmentation algorithms. Our approach restricts a graph-cut by using additional user-defined seed points to set up fixed nodes in the graph. The advantage is that manual edits can be integrated intuitively and quickly into the segmentation result of a graph-based approach. The method can be applied to both 2D and 3D objects that have to be segmented. Experimental results for synthetic and real images are presented to demonstrate the feasibility of our approach.

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