Blind Separation of Multichannel Biomedical Image Patterns by Non-negative Least-Correlated Component Analysis

  • Fa-Yu Wang
  • Yue Wang
  • Tsung-Han Chan
  • Chong-Yung Chi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4146)


Cellular and molecular imaging promises powerful tools for the visualization and elucidation of important disease-causing biological processes. Recent research aims to simultaneously assess the spatial-spectral/temporal distributions of multiple biomarkers, where the signals often represent a composite of more than one distinct source independent of spatial resolution. We report here a blind source separation method for quantitative dissection of mixed yet correlated biomarker patterns. The computational solution is based on a latent variable model, whose parameters are estimated using the non-negative least-correlated component analysis (nLCA) proposed in this paper. We demonstrate the efficacy of the nLCA with real bio-imaging data. With accurate and robust performance, it has powerful features which are of considerable widespread applicability.


Independent Component Analysis Blind Source Separation Latent Variable Model Blind Separation Blind Source Separation Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fa-Yu Wang
    • 1
    • 2
  • Yue Wang
    • 2
  • Tsung-Han Chan
    • 1
  • Chong-Yung Chi
    • 1
  1. 1.National Tsing Hua UniversityHsinchuTaiwan, ROC
  2. 2.Virginia Polytechnic Institute and State UniversityArlingtonUSA

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