Blind decomposition of low-dimensional multi-spectral image by sparse component analysis

Ivica Kopriva, Andrzej Cichocki

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

A multilayer hierarchical alternating least square nonnegative matrix factorization approach has been applied to blind decomposition of low-dimensional multi-spectral image. The method performs blind decomposition exploit-ing spectral diversity and spatial sparsity between materials present in the image and, unlike many blind source separation methods, is invariant with respect to statistical (in)dependence among spatial distributions of the materials. As opposed to many existing blind source separation algorithms, the method is capable of estimating the unknown number of materials present in the image. This number can be less than, equal to, or greater than the number of spectral bands. The method is validated on underdetermined blind source separation problems associated with blind decomposition of experimental red-green-blue images composed of four materials. Achieved performance has been superior when compared against methods based on minimization of the '1-norm: linear programming and interior-point methods. In addition to tumor demarcation, as demonstrated in the paper, other areas that can also benefit from the proposed method include cell, chemical, and tissue imaging.

Original languageEnglish
Pages (from-to)590-597
Number of pages8
JournalJournal of Chemometrics
Volume23
Issue number11
DOIs
Publication statusPublished - Nov 2009
Externally publishedYes

Keywords

  • Cell imaging
  • Chemical imaging
  • Factorization
  • Multi-spectral imaging
  • Nonnegative matrix
  • Sparse component analysis

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