Proceedings Paper
Sparseness constrained nonnegative matrix factorization for unsupervised 3D segmentation of multichannel images: demonstration on multispectral magnetic resonance image of the brain| Format | Member Price | Non-Member Price |
|---|---|---|
| $17.00 | $21.00 |
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
Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)
PDF: 8 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 866938 (13 March 2013); doi: 10.1117/12.2000529
Show Author Affiliations
Xinjian Chen, Soochow Univ. (China)
Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)
© SPIE. Terms of Use
