Research Article
A Novel Fusion Framework Based on Adaptive PCNN in NSCT Domain for Whole-Body PET and CT Images
Table 1
The average fusion performances of different methods on PET/CT data.
| Method | AG | EN | JE | CE | IQI | | |
| DWT | 2.95607 | 2.91867 | 4.47628 | 0.16158 | 0.77437 | 0.78363 | 0.62640 | NSCT | 1.77727 | 2.84131 | 4.50450 | 0.18274 | 0.82343 | 0.44651 | 0.42512 | NSCT_PCNN_1 | 2.35760 | 3.07476 | 5.32002 | 0.18201 | 0.78151 | 0.81062 | 0.56454 | NSCT_SF_PCNN | 2.90817 | 2.94637 | 5.30418 | 0.19419 | 0.77631 | 0.79662 | 0.49532 | NSCT_PCNN_2 | 2.45416 | 3.03202 | 5.36200 | 0.17807 | 0.79298 | 0.78664 | 0.48583 | NSCT_PCNN_3 | 2.58133 | 2.99083 | 5.30786 | 0.18161 | 0.78944 | 0.80226 | 0.59472 | The proposed framework | 2.90951 | 3.11046 | 5.49125 | 0.16308 | 0.79330 | 0.82769 | 0.69649 |
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