Sparseness constrained nonnegative matrix factorization for unsupervised 3D segmentation of multichannel images: demonstration on multispectral magnetic resonance image of the brain

@inproceedings{Kopriva2013SparsenessCN,
  title={Sparseness constrained nonnegative matrix factorization for unsupervised 3D segmentation of multichannel images: demonstration on multispectral magnetic resonance image of the brain},
  author={Ivica Kopriva and Ante Jukic and Xinjian Chen},
  booktitle={Medical Imaging},
  year={2013}
}
A method is proposed for unsupervised 3D (volume) segmentation of registered multichannel medical images. To this end, multichannel image is treated as 4D tensor represented by a multilinear mixture model, i.e. the image is modeled as weighted linear combination of 3D intensity distributions of organs (tissues) present in the image. Interpretation of this model suggests that 3D segmentation of organs (tissues) can be implemented through sparseness constrained factorization of the nonnegative… 

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Nonnegative matrix factorization: When data is not nonnegative

  • Siyuan WuJim Wang
  • Computer Science
    2014 7th International Conference on Biomedical Engineering and Informatics
  • 2014
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A new variations of the popular nonnegative matrix factorization (NMF) approach to extend it to the data with negative values by developing a new method that only allows W to contain nonnegative values, but allows both X and H to have both nonnegative and negative values.

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