
Proceedings Paper
Image segmentation using normalized cuts with multiple priorsFormat | Member Price | Non-Member Price |
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Paper Abstract
We present a novel method to incorporate prior knowledge into normalized cuts. The prior is incorporated into
the cost function by maximizing the similarity of the prior to one partition and the dissimilarity to the other. This
simple formulation can also be extended to multiple priors to allow the modeling of the shape variations. A shape
model obtained by PCA on a training set can be easily integrated into the new framework. This is in contrast
to other methods which usually incorporate the prior knowledge by hard constraints during optimization. The
eigenvalue problem inferred by spectral relaxation is not sparse, but can still be solved efficiently. We apply this
method to toy and real data and compare it with other normalized cut based segmentation algorithms and graph
cuts. We demonstrate that our method gives promising results and can still give a good segmentation even when
the prior is not accurate.
Paper Details
Date Published: 13 March 2013
PDF: 7 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 866937 (13 March 2013); doi: 10.1117/12.2000277
Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)
PDF: 7 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 866937 (13 March 2013); doi: 10.1117/12.2000277
Show Author Affiliations
Esmeralda Ruiz, Univ. Medical Ctr. Freiburg (Germany)
Marco Reisert, Univ. Medical Ctr. Freiburg (Germany)
Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)
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