Research Article
Medical Image Fusion Based on Feature Extraction and Sparse Representation
Table 2
The objective evaluation and running time for MR_T1 and MR_T2 image fused results of all methods.
| ā | | | | | MI |
| NSCT [1] | 0.6311 | 0.8178 | 0.6166 | 0.6374 | 5.4548 | NSCT [6] | 0.6316 | 0.8202 | 0.6192 | 0.6387 | 5.4252 | JSR [12] | 0.6732 | 0.8369 | 0.6367 | 0.5744 | 3.9141 | SR + NSCT [15] | 0.6896 | 0.8543 | 0.6313 | 0.6608 | 4.8713 | SR + NSCT [18] | 0.6741 | 0.8385 | 0.5563 | 0.5774 | 3.9210 | SR + SM | 0.6832 | 0.8520 | 0.7029 | 0.6676 | 5.3846 | SR + EM | 0.6911 | 0.8542 | 0.6989 | 0.6577 | 4.9104 | SR + SEM | 0.6991 | 0.8558 | 0.7063 | 0.6746 | 5.4563 |
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