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Proceedings Paper

Sparseness constrained nonnegative matrix factorization for unsupervised 3D segmentation of multichannel images: demonstration on multispectral magnetic resonance image of the brain
Author(s): Ivica Kopriva; Ante Jukić; Xinjian Chen
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Paper 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.

Paper Details

Date Published: 13 March 2013
PDF: 8 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 866938 (13 March 2013); doi: 10.1117/12.2000529
Show Author Affiliations
Ivica Kopriva, Ruđer Bošković Institute (Croatia)
Ante Jukić, Ruđer Bošković Institute (Croatia)
Xinjian Chen, Soochow Univ. (China)

Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)

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