Naslov
3D tensor-based blind multi-spectral image decomposition for tumor demarcation

Autori
Kopriva, Ivica ; Peršin, Antun

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

Izvornik
Proc. of SPIE Vol. 7623 / Dawant, Benoit M ; Haynord, David R - Bellingham (WA) : SPIE, 2010, 76231W-1

ISBN
978-0-8149-8031-6

Skup
Medical Imaging 2010: Image Processing

Mjesto i datum
San Diego (CA), Sjedinjene Američke Države, 13.02.2010. - 18.02.2010

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
3D tensor ; fluorescent multi-spectral image ; data clustering ; tumor demarcation

Sažetak
Blind decomposition of multi-spectral fluorescent image for tumor demarcation is formulated exploiting tensorial structure of the image. First contribution of the paper is identification of the matrix of spectral responses and 3D tensor of spatial distributions of the materials present in the image from Tucker3 or PARAFAC models of 3D image tensor. Second contribution of the paper is clustering based estimation of the number of the materials present in the image as well as matrix of their spectral profiles. 3D tensor of the spatial distributions of the materials is recovered through 3-mode multiplication of the multi-spectral image tensor and inverse of the matrix of spectral profiles. Tensor representation of the multi-spectral image preserves its local spatial structure that is lost, due to vectorization process, when matrix factorization-based decomposition methods (such as non-negative matrix factorization and independent component analysis) are used. Superior performance of the tensor-based image decomposition over matrix factorization-based decompositions is demonstrated on experimental red-green-blue (RGB) image with known ground truth as well as on RGB fluorescent images of the skin tumor (basal cell carcinoma).

Izvorni jezik
Engleski

Znanstvena područja
Matematika, Računarstvo, Kliničke medicinske znanosti

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