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Shape-Based Normalized Cuts Using Spectral Relaxation for Biomedical Segmentation | IEEE Journals & Magazine | IEEE Xplore

Shape-Based Normalized Cuts Using Spectral Relaxation for Biomedical Segmentation


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 ...Show More

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 that usually incorporate 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 biomedical data sets as well as natural images of people from a public database and compare it with other normalized cut based segmentation algorithms. We demonstrate that our method gives promising results and can still give a good segmentation even when the prior is not accurate.
Published in: IEEE Transactions on Image Processing ( Volume: 23, Issue: 1, January 2014)
Page(s): 163 - 170
Date of Publication: 28 October 2013

ISSN Information:

PubMed ID: 24184723

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