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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

Segmentation of artificial grid graph. (a) An artificial grid graph. (b) Graph with 5% Gaussian noise. (c) Graph with 10% Gaussian noise. (d) HAFSA on the graph with 5% Gaussian noise. (e) FCM on the graph with 5% Gaussian noise. (f) SFCM on the graph with 5% Gaussian noise. (g) HAFSA on the graph with 10% Gaussian noise. (h) FCM on the graph with 10% Gaussian noise. (i) SFCM on the graph with 10% Gaussian noise.
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fig3: Segmentation of artificial grid graph. (a) An artificial grid graph. (b) Graph with 5% Gaussian noise. (c) Graph with 10% Gaussian noise. (d) HAFSA on the graph with 5% Gaussian noise. (e) FCM on the graph with 5% Gaussian noise. (f) SFCM on the graph with 5% Gaussian noise. (g) HAFSA on the graph with 10% Gaussian noise. (h) FCM on the graph with 10% Gaussian noise. (i) SFCM on the graph with 10% Gaussian noise.

Mentions: In this section, we design the artificial grid graph to test the antinoise ability of HAFSA, FCM, and SFCM. Figure 3(a) demonstrates the artificial grid graph which consisted of 3 kinds of pixels: black (0), gray (127), and white pixels (255). The grid graph can be divided into 4 × 4, a total of 16, sublumps according to the pixel level. The artificial grid graph is an ideal simulation object which has clear boundaries between different lumps. In order to test antinoise ability, 5% Gaussian noise and 10% Gaussian noise have been added to the artificial grid graph and produced Figures 3(b) and 3(c). Figures 3(d), 3(e), and 3(f) are the segmentation results of HAFSA, FCM, and SFCM on 5% Gaussian noise grid graph. Figures 3(g), 3(h), and 3(i) are the segmentation results of HAFSA, FCM, and SFCM on 10% Gaussian noise grid graph.


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)

Segmentation of artificial grid graph. (a) An artificial grid graph. (b) Graph with 5% Gaussian noise. (c) Graph with 10% Gaussian noise. (d) HAFSA on the graph with 5% Gaussian noise. (e) FCM on the graph with 5% Gaussian noise. (f) SFCM on the graph with 5% Gaussian noise. (g) HAFSA on the graph with 10% Gaussian noise. (h) FCM on the graph with 10% Gaussian noise. (i) SFCM on the graph with 10% Gaussian noise.
© Copyright Policy
Related In: Results  -  Collection

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

fig3: Segmentation of artificial grid graph. (a) An artificial grid graph. (b) Graph with 5% Gaussian noise. (c) Graph with 10% Gaussian noise. (d) HAFSA on the graph with 5% Gaussian noise. (e) FCM on the graph with 5% Gaussian noise. (f) SFCM on the graph with 5% Gaussian noise. (g) HAFSA on the graph with 10% Gaussian noise. (h) FCM on the graph with 10% Gaussian noise. (i) SFCM on the graph with 10% Gaussian noise.
Mentions: In this section, we design the artificial grid graph to test the antinoise ability of HAFSA, FCM, and SFCM. Figure 3(a) demonstrates the artificial grid graph which consisted of 3 kinds of pixels: black (0), gray (127), and white pixels (255). The grid graph can be divided into 4 × 4, a total of 16, sublumps according to the pixel level. The artificial grid graph is an ideal simulation object which has clear boundaries between different lumps. In order to test antinoise ability, 5% Gaussian noise and 10% Gaussian noise have been added to the artificial grid graph and produced Figures 3(b) and 3(c). Figures 3(d), 3(e), and 3(f) are the segmentation results of HAFSA, FCM, and SFCM on 5% Gaussian noise grid graph. Figures 3(g), 3(h), and 3(i) are the segmentation results of HAFSA, FCM, and SFCM on 10% Gaussian noise grid graph.

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