Research Article
Morphological Reconstruction-Based Image-Guided Fuzzy Clustering with a Novel Impact Factor
Table 1
The ASA of tested algorithms on ST images with various noises.
| Noise | FCM | FCM_S1 | FCM_S2 | FCM + GF | IFCM_GF | FRFCM | MRIFCM_GF |
| 3% Gaussian | 0.7028 | 0.9783 | 0.9742 | 0.6984 | 0.7520 (0.047) | 0.9982 | 0.9993 (0.014) | 5% Gaussian | 0.6471 | 0.9166 | 0.8716 | 0.6646 | 0.7166 (0.034) | 0.9965 | 0.9987 (0.009) | 10% Gaussian | 0.5806 | 0.7628 | 0.7497 | 0.6172 | 0.7153 (0.02) | 0.9892 | 0.9964 (0.008) | 15% Gaussian | 0.5499 | 0.7292 | 0.7139 | 0.5924 | 0.7082 (0.017) | 0.9425 | 0.9907 (0.005) | 10% Salt & Pepper | 0.9431 | 0.9389 | 0.9826 | 0.9443 | 0.9995 (0.008) | 0.9991 | 0.9993 (0.009) | 20% Salt & Pepper | 0.8873 | 0.8757 | 0.9647 | 0.8916 | 0.9993 (0.006) | 0.9989 | 0.9993 (0.004) | 30% Salt & Pepper | 0.8304 | 0.7806 | 0.9409 | 0.8366 | 0.9981 (0.002) | 0.9976 | 0.9982 (0.003) |
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The best segmentation accuracy among the group. |