Blind separation of multichannel biomedical image patterns by non-negative least-correlated component analysis

Authors:
Fa-Yu Wang;Yue Wang;Tsung-Han Chan;Chong-Yung Chi
Affiliations:
National Tsing Hua University, Hsinchu, Taiwan, ROC;Virginia Polytechnic Institute and State University, Arlington, VA;National Tsing Hua University, Hsinchu, Taiwan, ROC;National Tsing Hua University, Hsinchu, Taiwan, ROC
Venue:
PRIB'06 Proceedings of the 2006 international conference on Pattern Recognition in Bioinformatics
Year:
2006

Citing 2
Cited 2

Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications

Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Blind separation of positive sources by globally convergent gradient search

Neural Computation

Blind separation of non-negative source signals using multiplicative updates and subspace projection

Signal Processing
Maximum contrast analysis for nonnegative blind source separation

Computers & Mathematics with Applications

Quantified Score

Hi-index	0.00

Visualization

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.