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An Effective Interactive Medical Image Segmentation Method using Fast GrowCut

Institution:
1Department of Computer Science, Stony Brook University, Stony Brook, NY, USA.
2Department of Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, AL, USA.
3Surgical Planning Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Publisher:
Int Conf Med Image Comput Comput Assist Interv. MICCAI 2014
Publication Date:
Sep-2014
Journal:
Int Conf Med Image Comput Comput Assist Interv.
Citation:
Int Conf Med Image Comput Comput Assist Interv. 2014 Sep;17(WS). Workshop on Interactive Methods.
Appears in Collections:
NAC, NA-MIC, SLICER, SPL
Sponsors:
P41 EB015902/EB/NIBIB NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Zhu L., Kolesov I., Gao Y., Kikinis R., Tannenbaum A. An Effective Interactive Medical Image Segmentation Method using Fast GrowCut. Int Conf Med Image Comput Comput Assist Interv. 2014 Sep;17(WS). Workshop on Interactive Methods.
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Segmentation of anatomical structures in medical imagery is a key step in a variety of clinical applications. Designing a generic, automated method that works for various structures and imaging modalities is a daunting task. In this paper, we present an effective interactive segmentation method that reformulates the GrowCut algorithm as a clustering problem and computes a fast, approximate solution. The method is further improved by using an efficient updating scheme requiring only local computations when new user input becomes available, making it applicable to high resolution images. The algorithm may easily be included as a user-oriented software module in any number of available medical imaging/image processing platforms such as 3D Slicer. The efficiency and effectiveness of the algorithm are demonstrated through tests on several challenging data sets where it is also compared to standard GrowCut.

Additional Material
1 File (102.508kB)
Zhu-MICCAI2014-fig5.jpg (102.508kB)