IEEE Trans Image Process - Shape-based normalized cuts using spectral relaxation for biomedical segmentation.

Tópicos

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Resumo

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.

Resumo Limpo

present novel method incorpor prior knowledg normal cut prior incorpor cost function maxim similar prior one partit dissimilar simpl formul can also extend multipl prior allow model shape variat shape model obtain pca train set can easili integr new framework contrast method usual incorpor prior knowledg hard constraint optim eigenvalu problem infer spectral relax spars can still solv effici appli method biomed data set well natur imag peopl public databas compar normal cut base segment algorithm demonstr method give promis result can still give good segment even prior accur

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