A Novel Blind Separation Method in Magnetic Resonance Images

Jianbin Gao,Ji Zhou,Li Du,Qi Xia,Yunfeng Fan,Lixue Yin

doi:10.1155/2014/726712

Abstract

A novel global search algorithm based method is proposed to separate MR images blindly in this paper. The key point of the method is the formulation of the new matrix which forms a generalized permutation of the original mixing matrix. Since the lowest entropy is closely associated with the smooth degree of source images, blind image separation can be formulated to an entropy minimization problem by using the property that most of neighbor pixels are smooth. A new dataset can be obtained by multiplying the mixed matrix by the inverse of the new matrix. Thus, the search technique is used to searching for the lowest entropy values of the new data. Accordingly, the separation weight vector associated with the lowest entropy values can be obtained. Compared with the conventional independent component analysis (ICA), the original signals in the proposed algorithm are not required to be independent. Simulation results on MR images are employed to further show the advantages of the proposed method.

Highlights

Blind source separation (BSS) aims at recovering unknown source signals only from the observed data
The digital imaging and communications in medicine (DICOM) standard was created by the National Electrical Manufacturers Association (NEMA) to aid the distribution and viewing of medical images, such as MR scans, and ultrasound
The brain has a number of constituents in the context of a MRI scan of the brain, such as gray matter, white matter, cerebrospinal fluid (CSF) fat, muscle/skin, and glial matter

Summary

Introduction

Blind source separation (BSS) aims at recovering unknown source signals only from the observed data. It has received considerable attention for its potential applications in a lot of fields, such as biomedical signal processing, image processing, and digital communications. The goal of a BSS algorithm is to find a weight matrix W such that ŝ(t) is a permutation of source signals s(t) up to a scaling factor. It is known that when n > m the principal component analysis (PCA) technique can be used to reduce the dimensionality of observations. For this reason, we only consider the case that n = m in this paper

Methods

Results

Discussion

Conclusion