Nonlinear Blind Source Separation Method for X-Ray Image Separation

Nuo ZHANG  Jianming LU  Takashi YAHAGI  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E89-A   No.4   pp.924-931
Publication Date: 2006/04/01
Online ISSN: 1745-1337
DOI: 10.1093/ietfec/e89-a.4.924
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Section on Selected Papers from the 18th Workshop on Circuits and Systems in Karuizawa)
blind source separation (BSS),  higher-order cumulants,  image separation,  radial basis function network (RBFN),  X-ray image,  

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In this study, we propose a robust approach for blind source separation (BSS) by using radial basis function networks (RBFNs) and higher-order statistics (HOS). The RBFN is employed to estimate the inverse of a hypothetical complicated mixing procedure. It transforms the observed signals into high-dimensional space, in which one can simply separate the transformed signals by using a cost function. Recently, Tan et al. proposed a nonlinear BSS method, in which higher-order moments between source signals and observations are matched in the cost function. However, it has a strict restriction that it requires the higher-order statistics of sources to be known. We propose a cost function that consists of higher-order cumulants and the second-order moment of signals to remove the constraint. The proposed approach has the capacity of not only recovering the complicated mixed signals, but also reducing noise from observed signals. Simulation results demonstrate the validity of the proposed approach. Moreover, a result of application to X-ray image separation also shows its practical applicability.