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

Authors:
Jan Egger;Rivka R. Colen;Bernd Freisleben;Christopher Nimsky
Affiliations:
Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA 02115 and Department of Mathematics and Computer Science, University of Mar ...;Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA 02115;Department of Mathematics and Computer Science, University of Marburg, Marburg, Germany 35032;Department of Neurosurgery, University of Marburg, Marburg, Germany 35043
Venue:
Journal of Medical Systems
Year:
2012

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Abstract

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.