3D tensor-based blind multispectral image decomposition for tumor demarcation

Ivica Kopriva; Antun Peršin

doi:10.1117/12.839568

12 March 2010 3D tensor-based blind multispectral image decomposition for tumor demarcation

Ivica Kopriva, Antun Peršin

Proceedings Volume 7623, Medical Imaging 2010: Image Processing; 76231W (2010) https://doi.org/10.1117/12.839568
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

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).

Citation Download Citation

Ivica Kopriva and Antun Peršin "3D tensor-based blind multispectral image decomposition for tumor demarcation", Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 76231W (12 March 2010); https://doi.org/10.1117/12.839568

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available