Abstract
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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Keywords
- Independent Component Analysis
- Blind Source Separation
- Latent Variable Model
- Blind Separation
- Blind Source Separation Algorithm
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© 2006 Springer-Verlag Berlin Heidelberg
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Wang, FY., Wang, Y., Chan, TH., Chi, CY. (2006). Blind Separation of Multichannel Biomedical Image Patterns by Non-negative Least-Correlated Component Analysis. In: Rajapakse, J.C., Wong, L., Acharya, R. (eds) Pattern Recognition in Bioinformatics. PRIB 2006. Lecture Notes in Computer Science(), vol 4146. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11818564_17
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DOI: https://doi.org/10.1007/11818564_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37446-6
Online ISBN: 978-3-540-37447-3
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