Laser & Optoelectronics Progress, Volume. 55, Issue 1, 11004(2018)
Image Segmentation Based on Adaptive Fuzzy C-Means and Post Processing Correction
Figures & Tables(15)
Fig. 2. Membership andcluster label of neighborhood. (a) membership of label 1; (b) membership of label 2; (c) cluster label
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
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
Fig. 5. Comparison of synthetic segmentation results in different neighborhoods. (a) NDFCM_P algorithm; (b) FNDFCM_P algorithm
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
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
Fig. 8. Comparison of #42049 segmentation results in different neighborhoods. (a) NDFCM_P algorithm; (b) FNDFCM_P algorithm
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
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
Table 1. Diagram of post processing
View tableTable 1. Diagram of post processing
Step 1: extraction of potentially misclassified pixels Step 2: reclassification of the extracted pixels (xl) 1 l← 1 1 for all extracted pixels xl do 2 for all pixels xj of the image do 2 for ∀ xj∈xl, do 3 if [label (xj)≠label (3×3 neighbourhood)] then 3 Find arg max (Ji) by using formula (23) 4 xl=xj 4 end for 5 l← l+1 5 end for 6 end if 6 return segmentation results 7 end for 8 return xl
Table 2. Parameters setting for different segmentation algorithms
View tableTable 2. Parameters setting for different segmentation algorithms
Algorithm Parameter setting m α λs λg 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 3. Comparison of indices of different segmentation algorithms under different noise levels
View tableTable 3. Comparison of indices of different segmentation algorithms under different noise levels
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 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 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 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 ARI 0.8002 0.8894 0.8919 0.9122 0.7901 0.9784 0.9720
Table 4. Comparison of indices of different segmentation algorithms on Berkeley image
View tableTable 4. Comparison of indices of different segmentation algorithms on Berkeley image
Image Noise level Index FCM_S1 FCM_S2 EnFCM FGFCM FLICM NDFCM_P FNDFCM_P #42049 Gaussian noise (0,0.05) SA 0.9407 0.9392 0.9417 0.9483 0.9529 0.9547 0.9539 ARI 0.8815 0.8783 0.8834 0.8966 0.9058 0.9094 0.9078 Salt & pepper noise (0.2) SA 0.8918 0.9529 0.8917 0.9465 0.9373 0.9601 0.9563 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 ARI 0.8144 0.8584 0.8295 0.8832 0.8908 0.9050 0.9062 #238001 Gaussian noise (0,0.02) SA 0.5926 0.5740 0.8938 0.9153 0.8447 0.9475 0.9495 ARI 0.3890 0.3611 0.8408 0.8731 0.7671 0.9212 0.9242 Salt & pepper noise (0.1) SA 0.7083 0.9071 0.7673 0.9146 0.6634 0.9610 0.9585 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 ARI 0.4428 0.4008 0.4888 0.9134 0.5547 0.9367 0.9368
Table 5. Comparison of execution time by different segmentation algorithms
View tableTable 5. Comparison of execution time by different segmentation algorithms
Image Size / (pixel×pixel) Cluster Time /s 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
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Zhu Zhanlong, Wang Junfen. Image Segmentation Based on Adaptive Fuzzy C-Means and Post Processing Correction[J]. Laser & Optoelectronics Progress, 2018, 55(1): 11004
Paper Information
Category: Image Processing
Received: Jun. 29, 2017
Accepted: --
Published Online: Sep. 10, 2018
The Author Email: Zhu Zhanlong (zzl_seu@163.com)
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