13 March 2013 Sparseness constrained nonnegative matrix factorization for unsupervised 3D segmentation of multichannel images: demonstration on multispectral magnetic resonance image of the brain
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Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 866938 (2013) https://doi.org/10.1117/12.2000529
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
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 matrix obtained by mode-4 unfolding of the 4D image tensor. Sparseness constraint implies that only one organ (tissue) is dominantly present at each pixel or voxel element. The method is preliminary validated, in term of Dice's coefficient, on extraction of brain tumor from synthetic multispectral magnetic resonance image obtained from the TumorSim database.
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Ivica Kopriva, Ivica Kopriva, Ante Jukić, Ante Jukić, Xinjian Chen, Xinjian Chen, } "Sparseness constrained nonnegative matrix factorization for unsupervised 3D segmentation of multichannel images: demonstration on multispectral magnetic resonance image of the brain ", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 866938 (13 March 2013); doi: 10.1117/12.2000529; https://doi.org/10.1117/12.2000529
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