Laser & Optoelectronics Progress, Volume. 57, Issue 2, 21001(2020)
Fuzzy C-Means Clustering Algorithm for Image Segmentation Insensitive to Cluster Size
Figures & Tables(9)
Fig. 1. Images, corresponding standard segmentation images, and gray-level histograms. (a)--(f) #NDT1--#NDT 6; (g)--(l) standard segmentation images #NDT1--#NDT 6; (m)--(r) gray-level histograms #NDT1--#NDT 6
Fig. 2. Segmentation results of different algorithms on #NDT1 image. (a) Image with Gaussian noise (0, 0.02); (b) FCM_S1 algorithm; (c) FCM_S2 algorithm; (d) EnFCM algorithm; (e) FGFCM algorithm; (f) algorithm in Ref. [20]; (g) algorithm in Ref. [21]; (h) IFCM_S1 algorithm; (i) IFCM_S2 algorithm
Fig. 3. Segmentation results of different algorithms on #NDT2 image. (a) Image with Gaussian noise (0, 0.01); (b) FCM_S1 algorithm; (c) FCM_S2 algorithm; (d) EnFCM algorithm; (e) FGFCM algorithm; (f) algorithm in Ref. [20]; (g) algorithm in Ref. [21]; (h) IFCM_S1 algorithm; (i) IFCM_S2 algorithm
Fig. 4. Segmentation results of different algorithms on #NDT3 image. (a) Image with Gaussian noise (0, 0.01); (b) FCM_S1 algorithm; (c) FCM_S2 algorithm; (d) EnFCM algorithm; (e) FGFCM algorithm; (f) algorithm in Ref. [20]; (g) algorithm in Ref. [21]; (h) IFCM_S1 algorithm; (i) IFCM_S2 algorithm
Fig. 5. Segmentation results of different algorithms on #NDT4 image. (a) Image with Gaussian noise (0, 0.01); (b) FCM_S1 algorithm; (c) FCM_S2 algorithm; (d) EnFCM algorithm; (e) FGFCM algorithm; (f) algorithm in Ref. [20]; (g) algorithm in Ref. [21]; (h) IFCM_S1 algorithm; (i) IFCM_S2 algorithm
Fig. 6. Segmentation results of different algorithms on #NDT5 image. (a) Image with Gaussian noise (0, 0.01); (b) FCM_S1 algorithm; (c) FCM_S2 algorithm; (d) EnFCM algorithm; (e) FGFCM algorithm; (f) algorithm in Ref. [20]; (g) algorithm in Ref. [21]; (h) IFCM_S1 algorithm; (i) IFCM_S2 algorithm
Fig. 7. Segmentation results of different algorithms on #NDT6 image. (a) Image with Gaussian noise (0, 0.01); (b) FCM_S1 algorithm; (c) FCM_S2 algorithm; (d) EnFCM algorithm; (e) FGFCM algorithm; (f) algorithm in Ref. [20]; (g) algorithm in Ref. [21]; (h) IFCM_S1 algorithm; (i) IFCM_S2 algorithm
Table 1. Parameter settings for different related algorithms
View tableTable 1. Parameter settings for different related algorithms
Algorithm Appearance Parameter setting m α λs λg T ε Local window FCM_S1 Ref. [13] 2 4 300 10-4 3×3 FCM_S2 Ref. [13] 2 4 300 10-4 3×3 EnFCM Ref. [14] 2 4 300 10-4 3×3 FGFCM Ref. [15] 2 3 3 300 10-4 3×3 Method in Ref. [20] Ref. [20] 2 300 10-4 Method in Ref. [21] Ref. [21] 2 300 10-4 IFCM_S1 This paper 2 4 300 10-4 3×3 IFCM_S2 This paper 2 4 300 10-4 3×3
Table 2. Comparison of segmentation indices of different algorithms on #NDT1~#NDT6 images
View tableTable 2. Comparison of segmentation indices of different algorithms on #NDT1~#NDT6 images
Image Noise level Index FCM_S1 FCM_S2 EnFCM FGFCM Method in Ref. [20] Method in Ref. [21] IFCM_S1 IFCM_S2 #NDT1 Unadded noise SA 0.9466 0.9467 0.9473 0.9459 0.9854 0.9877 0.9918 0.9916 ARI 0.8931 0.8933 0.8945 0.8918 0.9708 0.9753 0.9836 0.9832 Gaussian Noise(0,0.02) SA 0.8516 0.8149 0.8721 0.8876 0.8781 0.7768 0.9765 0.9692 ARI 0.7032 0.6298 0.7443 0.7751 0.7561 0.5537 0.9530 0.9383 Salt & pepper noise(0.1) SA 0.8440 0.9137 0.8532 0.9344 0.9510 0.8512 0.9717 0.9859 ARI 0.6881 0.8275 0.7063 0.8688 0.9020 0.7024 0.9434 0.9718 #NDT2 Unadded noise SA 0.8338 0.8616 0.8333 0.8556 0.9777 0.9621 0.9225 0.9554 ARI 0.6676 0.7233 0.6667 0.7112 0.9554 0.9242 0.8449 0.9109 Gaussian Noise(0,0.01) SA 0.7571 0.7395 0.7712 0.7730 0.8342 0.7709 0.9104 0.9291 ARI 0.5143 0.4790 0.5423 0.5459 0.6684 0.5419 0.8209 0.8583 Salt & pepper noise(0.1) SA 0.7170 0.8556 0.7257 0.8228 0.9795 0.9875 0.9113 0.9543 ARI 0.4340 0.7112 0.4514 0.6457 0.9590 0.9750 0.8226 0.9086 #NDT3 Unadded noise SA 0.5113 0.5826 0.5086 0.6226 0.9916 0.9916 0.9887 0.9916 ARI 0.0227 0.1653 0.0173 0.2451 0.9831 0.9831 0.9773 0.9833 Gaussian Noise(0,0.01) SA 0.5835 0.6131 0.5934 0.6496 0.9609 0.7603 0.9844 0.9856 ARI 0.1670 0.2261 0.1869 0.2992 0.9217 0.5206 0.9689 0.9713 Salt & pepper noise(0.1) SA 0.5996 0.5831 0.6360 0.6185 0.8983 0.8982 0.9837 0.9895 ARI 0.1991 0.1662 0.2720 0.2371 0.7965 0.7964 0.9674 0.9790 #NDT4 Unadded noise SA 0.9210 0.9666 0.9234 0.9672 0.9760 0.9798 0.9638 0.9850 ARI 0.8419 0.9332 0.8467 0.9344 0.9520 0.9596 0.9275 0.9696 Gaussian Noise(0,0.01) SA 0.8109 0.8103 0.8321 0.8633 0.8657 0.8337 0.9544 0.9606 ARI 0.6218 0.6206 0.6643 0.7267 0.7315 0.6675 0.9088 0.9212 Salt & pepper noise(0.1) SA 0.8203 0.9208 0.7923 0.9284 0.9540 0.9538 0.9356 0.9770 ARI 0.6407 0.8415 0.5846 0.8567 0.9080 0.9076 0.8713 0.9540 #NDT5 Unadded noise SA 0.9009 0.9290 0.9011 0.9296 0.9633 0.9593 0.9313 0.9580 ARI 0.8017 0.8580 0.8023 0.8591 0.9266 0.9186 0.8626 0.9160 Gaussian Noise(0,0.01) SA 0.8620 0.8717 0.8614 0.8977 0.8516 0.8316 0.9291 0.9274 ARI 0.7240 0.7434 0.7229 0.7954 0.7031 0.6631 0.8583 0.8549 Salt & pepper noise(0.1) SA 0.8156 0.8981 0.8117 0.9103 0.9060 0.9047 0.9011 0.9384 ARI 0.6311 0.7963 0.6234 0.8206 0.8120 0.8094 0.8022 0.8769 Image Noise level Index FCM_S1 FCM_S2 EnFCM FGFCM Method in Ref. [20] Method in Ref. [21] IFCM_S1 IFCM_S2 #NDT6 Unadded noise SA 0.9477 0.9427 0.9485 0.9472 0.9692 0.9636 0.9783 0.9739 ARI 0.8954 0.8854 0.8971 0.8945 0.9384 0.9273 0.9566 0.9478 Gaussian Noise(0,0.01) SA 0.9217 0.9181 0.9243 0.9333 0.8605 0.8455 0.9679 0.9662 ARI 0.8434 0.8362 0.8487 0.8666 0.7211 0.6910 0.9358 0.9323 Salt & pepper noise(0.1) SA 0.8894 0.9281 0.8902 0.9371 0.8980 0.8911 0.9475 0.9562 ARI 0.7787 0.8563 0.7805 0.8743 0.7960 0.7822 0.8949 0.9125
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Zhao Zhanmin, Zhu Zhanlong, Liu Yongjun, Liu Ming, Zheng Yibo. Fuzzy C-Means Clustering Algorithm for Image Segmentation Insensitive to Cluster Size[J]. Laser & Optoelectronics Progress, 2020, 57(2): 21001
Paper Information
Category: Image Processing
Received: May. 20, 2019
Accepted: --
Published Online: Jan. 3, 2020
The Author Email: Zhanlong Zhu (zzl_seu@163.com)
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