Blind Separation Method of Brain Image Data Based on Dictionary Sparsity
Feng Ba · Jisuanji gongcheng · 2015
As the Independent Components Analysis(ICA)is difficult to be fully satisfied in real brain image analysis.According to the characteristics of function Magnetic Resonance Imaging(fMRI)data,this paper proposes a new blind separation method,which exploits sparsity of sources in a dictionary,for brain image data.The proposed method combines dictionary learning and blind source separation technique.By exploiting sparsity of source component in a signal dictionary,the blind separation process is converted to the transformed sparse domain.It uses experiments of brain activation localization to evaluate the proposed method.Experimental results show that,compared with ICA based method,voxels selected by the proposed method are more related to task function and more concentrated in spatial distribution.The proposed method has high quality solution and the solving efficiency can reliably process the brain image data.Estimating source component in the sparse domain is helpful for improving quality of blind separation equality.