Naslov
Sparseness Constrained Nonnegative Matrix Factorization for Unsupervised 3D Segmentation of Multichannel Images: Demonstration on Multispectral Magnetic Resonance Image of the Brain

Autori
Kopriva, Ivica ; Jukić, Ante ; Chen, Xinjian

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), ostalo

Izvornik
Proceedings of the SPIE on CD-ROM volume 8669 / Ourselin, Sebastian ; Haynor, R. David - Bellingham (WA) : SPIE, 2013, 866938-1

ISBN
978-0-8194-9451-1

Skup
Medical Imaging 2013: Image Processing

Mjesto i datum
Lake Buena Vista (FL), Sjedinjene Američke Države, 09.02.2013. - 14.02.2013

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Multispectral magnetic resonance image; brain tumor delineation; unsupervised segmentation; sparseness; nonnegative matrix factorization.

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Matematika, Računarstvo, Kliničke medicinske znanosti

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