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A Hybrid Method for Image Segmentation Based on Artificial Fish Swarm Algorithm and Fuzzy c-Means Clustering.

Ma L, Li Y, Fan S, Fan R - Comput Math Methods Med (2015)

Bottom Line: Fuzzy c-means (FCM) clustering is one of the popular clustering algorithms for medical image segmentation.Meanwhile, Metropolis criterion and noise reduction mechanism are introduced to AFSA for enhancing the convergence rate and antinoise ability.A number of evaluation indicators also demonstrate that the effect of HAFSA is more excellent than FCM and suppressed FCM (SFCM).

View Article: PubMed Central - PubMed

Affiliation: School of Information Science and Technology, Jinan University, Guangzhou 510632, China.

ABSTRACT
Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM) clustering is one of the popular clustering algorithms for medical image segmentation. However, FCM has the problems of depending on initial clustering centers, falling into local optimal solution easily, and sensitivity to noise disturbance. To solve these problems, this paper proposes a hybrid artificial fish swarm algorithm (HAFSA). The proposed algorithm combines artificial fish swarm algorithm (AFSA) with FCM whose advantages of global optimization searching and parallel computing ability of AFSA are utilized to find a superior result. Meanwhile, Metropolis criterion and noise reduction mechanism are introduced to AFSA for enhancing the convergence rate and antinoise ability. The artificial grid graph and Magnetic Resonance Imaging (MRI) are used in the experiments, and the experimental results show that the proposed algorithm has stronger antinoise ability and higher precision. A number of evaluation indicators also demonstrate that the effect of HAFSA is more excellent than FCM and suppressed FCM (SFCM).

No MeSH data available.


Related in: MedlinePlus

(a) The variation of the c-means objective function J on HAFSA, FCM, and SFCM with Gaussian noises. (b) The variation of the c-means objective function J on HAFSA, FCM, and SFCM with Speckle noises.
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fig4: (a) The variation of the c-means objective function J on HAFSA, FCM, and SFCM with Gaussian noises. (b) The variation of the c-means objective function J on HAFSA, FCM, and SFCM with Speckle noises.

Mentions: Figures 4(a) and 4(b) show curves of the relationship between c-means objective function J and the intensity of Gaussian noise and Speckle noise. According to Figures 4(a) and 4(b), the value J of 3 algorithms increases with the increasing intensity of Gaussian noise or Speckle noise. Under the same noise intensity situation, HAFSA is superior (with smaller J) to SFCM obviously, and SFCM algorithm is superior to FCM algorithm slightly. With the increasing of the Gaussian noise, J increases rapidly for SFCM and FCM but slowly for HAFSA (note that in Figure 4(a)y-axis is uneven; the spacing of y-axis corresponding by the lower red line is 0.2 times that of the upper red line). Figures 4(a) and 4(b) represent that HAFSA is an ideal image segmentation method with strong and robust antinoise ability.


A Hybrid Method for Image Segmentation Based on Artificial Fish Swarm Algorithm and Fuzzy c-Means Clustering.

Ma L, Li Y, Fan S, Fan R - Comput Math Methods Med (2015)

(a) The variation of the c-means objective function J on HAFSA, FCM, and SFCM with Gaussian noises. (b) The variation of the c-means objective function J on HAFSA, FCM, and SFCM with Speckle noises.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4663300&req=5

fig4: (a) The variation of the c-means objective function J on HAFSA, FCM, and SFCM with Gaussian noises. (b) The variation of the c-means objective function J on HAFSA, FCM, and SFCM with Speckle noises.
Mentions: Figures 4(a) and 4(b) show curves of the relationship between c-means objective function J and the intensity of Gaussian noise and Speckle noise. According to Figures 4(a) and 4(b), the value J of 3 algorithms increases with the increasing intensity of Gaussian noise or Speckle noise. Under the same noise intensity situation, HAFSA is superior (with smaller J) to SFCM obviously, and SFCM algorithm is superior to FCM algorithm slightly. With the increasing of the Gaussian noise, J increases rapidly for SFCM and FCM but slowly for HAFSA (note that in Figure 4(a)y-axis is uneven; the spacing of y-axis corresponding by the lower red line is 0.2 times that of the upper red line). Figures 4(a) and 4(b) represent that HAFSA is an ideal image segmentation method with strong and robust antinoise ability.

Bottom Line: Fuzzy c-means (FCM) clustering is one of the popular clustering algorithms for medical image segmentation.Meanwhile, Metropolis criterion and noise reduction mechanism are introduced to AFSA for enhancing the convergence rate and antinoise ability.A number of evaluation indicators also demonstrate that the effect of HAFSA is more excellent than FCM and suppressed FCM (SFCM).

View Article: PubMed Central - PubMed

Affiliation: School of Information Science and Technology, Jinan University, Guangzhou 510632, China.

ABSTRACT
Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM) clustering is one of the popular clustering algorithms for medical image segmentation. However, FCM has the problems of depending on initial clustering centers, falling into local optimal solution easily, and sensitivity to noise disturbance. To solve these problems, this paper proposes a hybrid artificial fish swarm algorithm (HAFSA). The proposed algorithm combines artificial fish swarm algorithm (AFSA) with FCM whose advantages of global optimization searching and parallel computing ability of AFSA are utilized to find a superior result. Meanwhile, Metropolis criterion and noise reduction mechanism are introduced to AFSA for enhancing the convergence rate and antinoise ability. The artificial grid graph and Magnetic Resonance Imaging (MRI) are used in the experiments, and the experimental results show that the proposed algorithm has stronger antinoise ability and higher precision. A number of evaluation indicators also demonstrate that the effect of HAFSA is more excellent than FCM and suppressed FCM (SFCM).

No MeSH data available.


Related in: MedlinePlus