Table 2.

The ASA of tested algorithms on SF images with various noises.

Noise	FCM	FCM_S1	FCM_S2	FCM + GF (ε=0.14)	IFCM_GF (ρ)	FRFCM	MRIFCM_GF (ρ)
3% Gaussian	0.7277	0.9716	0.9736	0.7333	0.9274 (0.02)	0.9972	∗ 0.9986 (0.019)
5% Gaussian	0.6417	0.9301	0.9271	0.6486	0.7492 (0.04)	0.9952	∗ 0.9979 (0.016)
10% Gaussian	0.5392	0.8103	0.8003	0.5250	0.6132 (0.002)	0.9857	∗ 0.9944 (0.004)
15% Gaussian	0.4902	0.7331	0.7348	0.4709	0.5620 (0.015)	0.9592	∗ 0.9847 (0.001)
10% Salt & Pepper	0.9233	0.9329	0.9746	0.9273	∗ 0.9995 (0.003)	0.9990	0.9994 (0.007)
20% Salt & Pepper	0.8475	0.8617	0.9477	0.8620	∗ 0.9989 (0.002)	0.9982	∗ 0.9989 (0.005)
30% Salt & Pepper	0.7708	0.7613	0.9152	0.7958	0.9973 (0.001)	0.9966	∗ 0.9975 (0.003)

^∗The best segmentation accuracy among the group.