Image Segmentation Based on Adaptive Fuzzy C-Means and Post Processing Correction

Zhu Zhanlong; Wang Junfen

doi:10.3788/LOP55.011004

Journals >Laser & Optoelectronics Progress >Volume 55 >Issue 1 >Page 11004 > Article

Laser & Optoelectronics Progress
Vol. 55, Issue 1, 11004 (2018)

Image Segmentation Based on Adaptive Fuzzy C-Means and Post Processing Correction

Zhu Zhanlong^1、2、* and Wang Junfen¹

Author Affiliations

¹School of Information Engineering, Hebei GEO University, Shijiazhuang, Hebei 0 50031, China

²Hebei Key Laboratory of Optoelectronic Information and Geo-Detection Technology, Hebei GEO University,Shijiazhuang, Hebei 0 50031, China

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DOI: 10.3788/LOP55.011004 Cite this Article Set citation alerts

Zhu Zhanlong, Wang Junfen. Image Segmentation Based on Adaptive Fuzzy C-Means and Post Processing Correction[J]. Laser & Optoelectronics Progress, 2018, 55(1): 11004 Copy Citation Text

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Fig. 1. 3×3 window with noise. (a)(b) Gaussian noise; (c)(d) mixed noise

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Fig. 2. Membership andcluster label of neighborhood. (a) membership of label 1; (b) membership of label 2; (c) cluster label

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Fig. 3. Segmentation of synthetic image with Gaussian noise(0, 0.03). (a) Original image; (b) image with Gaussian noise (0, 0.03); (c) FCM_S1algorithm; (d) FCM_S2 algorithm; (e) EnFCM algorithm; (f) FGFCM algorithm; (g) FLICM algorithm; (h) NDFCM_P algorithm; (i) FNDFCM_P algorithm

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Fig. 4. Segmentation of synthetic image with salt & pepper noise (0.1). (a) Original image; (b) image with salt & pepper noise (0.1); (c) FCM_S1 algorithm; (d) FCM_S2 algorithm; (e) EnFCM algorithm; (f) FGFCM algorithm; (g) FLICM algorithm; (h) NDFCM_P algorithm; (i) FNDFCM_P algorithm

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Fig. 5. Comparison of synthetic segmentation results in different neighborhoods. (a) NDFCM_P algorithm; (b) FNDFCM_P algorithm

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Fig. 6. Segmentation results of #42049 (a) Original image; (b) image with mixed noise; (c) standard manual segmentation; (d) FCM_S1 algorithm; (e) FCM_S2 algorithm; (f) EnFCM algorithm; (g) FGFCM algorithm; (h) FLICM algorithm; (i) NDFCM_P algorithm; (j) FNDFCM_P algorithm

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Fig. 7. Segmentation results of #238001. (a) Original image; (b) image with Salt & Pepper noise; (c) standard manual segmentation; (d) FCM_S1 algorithm;(e) FCM_S2 algorithm; (f) EnFCM algorithm; (g) FGFCM algorithm; (h) FLICM algorithm; (i) NDFCM_P algorithm; (j) FNDFCM_P algorithm

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Fig. 8. Comparison of #42049 segmentation results in different neighborhoods. (a) NDFCM_P algorithm; (b) FNDFCM_P algorithm

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Fig. 9. Segmentation of stone mountain image by different algorithms. (a) Original image; (b) image corrupted by mixed noise; (c) FCM_S1algorithm; (d) FCM_S2 algorithm; (e) EnFCM algorithm; (f) FGFCM algorithm; (g) FLICM algorithm; (h) NDFCM_P algorithm; (i) FNDFCM_P algorithm

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Fig. 10. Segmentation of coin image by different algorithms. (a) Original image; (b) image corrupted by salt & pepper noise; (c) FCM_S1 algorithm; (d) FCM_S2 algorithm; (e) EnFCM algorithm; (f) FGFCM algorithm; (g) FLICM algorithm; (h) NDFCM_P algorithm; (i) FNDFCM_P algorithm

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Step 1: extraction of potentially misclassified pixels	Step 2: reclassification of the extracted pixels (x_l)
1 l← 1	1 for all extracted pixels x_l do
2 for all pixels x_j of the image do	2 for ∀ x_j∈x_l, do
3 if [label (x_j)≠label (3×3 neighbourhood)] then	3 Find arg max (J_i) by using formula (23)
4 x_l=x_j	4 end for
5 l← l+1	5 end for
6 end if	6 return segmentation results
7 end for
8 return x_l

Table 1. Diagram of post processing

Algorithm	Parameter setting
Algorithm	m	α	λ_s	λ_g	T	ε
FCM_S1	2	4			300	10^-5
FCM_S2	2	4			300	10^-5
EnFCM	2	4			300	10^-5
FGFCM	2		3	3	300	10^-5
FLICM	2				300	10^-5
NDFCM_P	2		3	3	300	10^-5
FNDFCM_P	2		3	3	300	10^-5

Table 2. Parameters setting for different segmentation algorithms

Noise level	Index	FCM_S1	FCM_S2	EnFCM	FGFCM	FLICM	NDFCM_P	FNDFCM_P
Gaussian noise (0,0.03)	SA	0.9224	0.9358	0.9249	0.9476	0.9543	0.9845	0.9825
Gaussian noise (0,0.03)	ARI	0.8966	0.9144	0.8999	0.9302	0.9391	0.9792	0.9767
Gaussian noise (0,0.04)	SA	0.8957	0.8899	0.8983	0.9257	0.9413	0.9731	0.9714
Gaussian noise (0,0.04)	ARI	0.8613	0.8666	0.8644	0.9011	0.9217	0.9641	0.9619
Salt & pepper noise (0.1)	SA	0.8962	0.9586	0.9575	0.9703	0.8725	0.9966	0.9934
Salt & pepper noise (0.1)	ARI	0.8616	0.9448	0.9434	0.9604	0.8300	0.9954	0.9911
Gaussian noise (0,0.02) &salt & pepper noise (0.1)	SA	0.8502	0.9170	0.9189	0.9341	0.8426	0.9837	0.9790
Gaussian noise (0,0.02) &salt & pepper noise (0.1)	ARI	0.8002	0.8894	0.8919	0.9122	0.7901	0.9784	0.9720

Table 3. Comparison of indices of different segmentation algorithms under different noise levels

Image	Noise level	Index	FCM_S1	FCM_S2	EnFCM	FGFCM	FLICM	NDFCM_P	FNDFCM_P
#42049	Gaussiannoise (0,0.05)	SA	0.9407	0.9392	0.9417	0.9483	0.9529	0.9547	0.9539
	Gaussiannoise (0,0.05)	ARI	0.8815	0.8783	0.8834	0.8966	0.9058	0.9094	0.9078
	Salt & peppernoise (0.2)	SA	0.8918	0.9529	0.8917	0.9465	0.9373	0.9601	0.9563
	Salt & peppernoise (0.2)	ARI	0.7836	0.9058	0.7833	0.8929	0.8346	0.9202	0.9127
	Gaussian noise(0,0.04) & Salt &pepper noise (0.1)	SA	0.9072	0.9292	0.9147	0.9416	0.9454	0.9525	0.9531
	Gaussian noise(0,0.04) & Salt &pepper noise (0.1)	ARI	0.8144	0.8584	0.8295	0.8832	0.8908	0.9050	0.9062
#238001	Gaussiannoise (0,0.02)	SA	0.5926	0.5740	0.8938	0.9153	0.8447	0.9475	0.9495
	Gaussiannoise (0,0.02)	ARI	0.3890	0.3611	0.8408	0.8731	0.7671	0.9212	0.9242
	Salt & peppernoise (0.1)	SA	0.7083	0.9071	0.7673	0.9146	0.6634	0.9610	0.9585
	Salt & peppernoise (0.1)	ARI	0.5625	0.8606	0.6509	0.8719	0.4951	0.9415	0.9378
	Gaussian noise(0,0.01) & Salt &pepper noise (0.05)	SA	0.6285	0.6005	0.6592	0.9422	0.7031	0.9578	0.9578
	Gaussian noise(0,0.01) & Salt &pepper noise (0.05)	ARI	0.4428	0.4008	0.4888	0.9134	0.5547	0.9367	0.9368

Table 4. Comparison of indices of different segmentation algorithms on Berkeley image

Image	Size /(pixel×pixel)	Cluster	Time /s
Image	Size /(pixel×pixel)	Cluster	FCM_S1	EnFCM	FLICM	NDFCM_P	FNDFCM_P
Synthetic image	128×128	4	0.25	0.06	16.59	0.95	0.81
#42049 image	481×321	2	0.76	0.04	121.63	108.61	112.28
Coin image	308×242	3	4.52	0.04	39.46	29.65	30.80

Table 5. Comparison of execution time by different segmentation algorithms

Zhu Zhanlong, Wang Junfen. Image Segmentation Based on Adaptive Fuzzy C-Means and Post Processing Correction[J]. Laser & Optoelectronics Progress, 2018, 55(1): 11004

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