Research Article
Multimodal MRI Brain Tumor Image Segmentation Using Sparse Subspace Clustering Algorithm
Table 2
Comparison of multimodal image segmentation results.
| Experimental sample | Index | Dice | Jaccard | Precision | Recall |
| Malignant tumor | 1 | 0.8923 | 0.8042 | 0.9627 | 0.8287 | 2 | 0.8596 | 0.7752 | 0.9748 | 0.7789 | 3 | 0.8385 | 0.7431 | 0.9263 | 0.7821 | 4 | 0.9507 | 0.8785 | 0.9874 | 0.9264 | 5 | 0.9310 | 0.7886 | 0.9845 | 0.7954 | 6 | 0.8746 | 0.7964 | 0.9678 | 0.8103 | 7 | 0.9152 | 0.8522 | 0.9034 | 0.9371 | 8 | 0.8386 | 0.7371 | 0.9976 | 0.7352 | 9 | 0.8731 | 0.7796 | 0.9948 | 0.7911 | 10 | 0.8694 | 0.7649 | 0.9563 | 0.8002 | 11 | 0.8627 | 0.7628 | 0.9915 | 0.7832 | 12 | 0.7016 | 0.5364 | 0.9997 | 0.5349 | 13 | 0.8018 | 0.6742 | 0.9306 | 0.7132 | 14 | 0.8220 | 0.6976 | 0.9736 | 0.7120 | 15 | 0.8129 | 0.6842 | 0.9637 | 0.7058 | Bright tumor | 1 | 0.7961 | 0.6425 | 0.9264 | 0.6779 | 2 | 0.8129 | 0.8413 | 0.9779 | 0.8646 | 3 | 0.9298 | 0.6624 | 0.9836 | 0.6732 | 4 | 0.9401 | 0.6830 | 0.7375 | 0.9118 | 5 | 0.9228 | 0.8698 | 0.9990 | 0.8769 | 6 | 0.9418 | 0.8891 | 0.9862 | 0.8996 | 7 | 0.7147 | 0.5510 | 0.9996 | 0.5534 | 8 | 0.7753 | 0.6256 | 0.9676 | 0.6394 | 9 | 0.9027 | 0.8244 | 0.9834 | 0.8426 | 10 | 0.8624 | 0.7632 | 0.9623 | 0.7824 | Mean | | 0.8577 | 0.7451 | 0.9615 | 0.7743 |
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