A novel blind separation method in magnetic resonance images.
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.
School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China ; Key Laboratory of Integrated Electronic System, Ministry of Education, Chengdu 611731, China ; Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu 610072, China.
Jianbin
J
Gao
School of Computer Science & Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Qi
Q
Xia
Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu 610072, China.
Lixue
L
Yin
School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China.
Ji
J
Zhou
Southwest China Research Institute of Electronic Equipment, Chengdu 610036, China.
Li
L
Du
School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China.
Yunfeng
0000-0002-6551-9394
Y
Fan
D000465:Algorithms; D001921:Brain / Q000473:pathology; D001931:Brain Mapping / Q000379:methods; D003198:Computer Simulation; D019277:Entropy; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted