3D Liver Tumor Segmentation in CT Images Using Improved Fuzzy C-Means and Graph Cuts

Table 2

Statistical performance of the proposed method compared to some other methods. Results are represented as mean ± standard deviation.

Method	Year	Auto	Dataset	VOE (%)	RVD (%)	ASD (mm)	RMSD (mm)	MaxD (mm)	DICE	Runtime
Stawiaski et al. [14]	2008	Interactive	LTSC08	29.49 ± 12.80	23.87 ± 34.72	1.50 ± 0.67	2.07 ± 0.89	8.30 ± 4.10	—	5–8 mins
Li et al. [12]	2012	Semi	LTSC08	26.31 ± 5.79	−10.64 ± 7.55	1.06 ± 0.38	—	8.66 ± 3.17	—	30 s
Kadoury et al. [17]	2015	Auto	LTSC08	25.2 ± 1.7	14.3 ± 2.8	1.4 ± 0.3	1.6 ± 0.4	6.9 ± 1.8	—	102 s
Moghbel et al. [10]	2016	Auto	3Dircadb	22.78 ± 12.15	8.59 ± 18.78	—	—	—	0.75 ± 0.15	30 s/slice
Foruzan and Chen [19]	2016	Semi	3Dircadb	30.61 ± 10.44	15.97 ± 12.04	4.18 ± 9.60	5.09 ± 10.71	12.55 ± 17.07	0.82 ± 0.07	154 s
Our method		Semi	3Dircadb	29.04 ± 8.16	−2.20 ± 15.88	0.72 ± 0.33	1.10 ± 0.49	4.25 ± 3.03	0.83 ± 0.06	45 s