Paper Info
Title
3D tensor-based blind multispectral image decomposition for tumor demarcation
Abstract
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).
Year
DOI
Venue
2010
10.1117/12.839568
Proceedings of SPIE
Keywords
Field
DocType
3D tensor,fluorescent multi-spectral image,data clustering,tumor demarcation
Computer vision,Tensor,Matrix (mathematics),Multispectral image,Matrix decomposition,Vectorization (mathematics),Artificial intelligence,RGB color model,Independent component analysis,Structure tensor,Physics
Conference
Volume
ISSN
Citations 
7623
0277-786X
0
PageRank 
References 
Authors
0.34
4
2
Name
Order
Citations
PageRank
Ivica Kopriva114616.60
antun persin200.68