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

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.

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