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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

The flowchart of AFSA.
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fig1: The flowchart of AFSA.

Mentions: Let G = {0,1,…, L − 1} denote the gray levels, where L = 256 and gray levels are from 0 to 255. In the process of image segmentation, fish's location coding is a pixel gray value vector V = (v1, v2,…,vc)T which consisted of c cluster centers. The c-means objective function serves as food concentration of artificial fish; that is, F(V) = J(V). As shown in Figure 1, AFSA mainly adjusts the position of the artificial fish by swarm behavior and follow behavior. AFSA will be terminated when it converges or achieves the maximum generation Maxgen. The four basic behaviors are described below.


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)

The flowchart of AFSA.
© Copyright Policy
Related In: Results  -  Collection

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

fig1: The flowchart of AFSA.
Mentions: Let G = {0,1,…, L − 1} denote the gray levels, where L = 256 and gray levels are from 0 to 255. In the process of image segmentation, fish's location coding is a pixel gray value vector V = (v1, v2,…,vc)T which consisted of c cluster centers. The c-means objective function serves as food concentration of artificial fish; that is, F(V) = J(V). As shown in Figure 1, AFSA mainly adjusts the position of the artificial fish by swarm behavior and follow behavior. AFSA will be terminated when it converges or achieves the maximum generation Maxgen. The four basic behaviors are described below.

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