Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1155/2014/726712 | COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE |
Keywords | Field | DocType |
algorithms,linear models,principal component analysis,magnetic resonance imaging,computer simulation,brain mapping,entropy | Computer vision,Inverse,Search algorithm,Matrix (mathematics),Permutation,Weight,Independent component analysis,Pixel,Artificial intelligence,Mathematics,Principal component analysis,Machine learning | Journal |
Volume | Issue | ISSN |
2014 | null | 1748-670X |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianbin Gao | 1 | 66 | 5.18 |
| Qi Xia | 2 | 132 | 21.76 |
| Lixue Yin | 3 | 0 | 0.68 |
| Ji Zhou | 4 | 263 | 18.83 |
| Li Du | 5 | 46 | 13.40 |
| Yunfeng Fan | 6 | 0 | 0.34 |