Table 3.
The ASA of tested algorithms with various noises on Brain images.
| Noise | FCM | FCM_S1 | FCM_S2 | FCM + G (ε = 0.14) | IFCM_GF (ρ) | FRFCM | MRIFCM_GF (ρ) |
|---|---|---|---|---|---|---|---|
| 5% Rician | 0.9993 | 0.9953 | 0.9908 | 0.9998 | ∗ 0.9999 (0.15) | 0.9837 | 0.9952 (0.14) |
| 10% Rician | 0.8427 | ∗ 0.9866 | 0.9772 | 0.8480 | 0.9380 (0.15) | 0.9762 | 0.9844 (0.1) |
| 15% Rician | 0.7075 | 0.9085 | 0.8313 | 0.7265 | 0.8167 (0.05) | 0.9387 | ∗ 0.9483 (0.08) |
| 20% Rician | 0.6240 | 0.7993 | 0.7823 | 0.6456 | 0.7731 (0.03) | 0.8984 | ∗ 0.9152 (0.08) |
| 25% Rician | 0.5540 | 0.7674 | 0.7477 | 0.5862 | 0.7410 (0.02) | 0.8631 | ∗ 0.8745 (0.07) |
∗The best segmentation accuracy among the group.