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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

Noise pixel and neighbor regions.
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fig2: Noise pixel and neighbor regions.

Mentions: Most noises are Gaussian noise and salt and pepper noise in medical images. Noises generally locate in the same cluster or boundary of several different clusters. The gray image is mostly like a grid network where each pixel possesses 8 neighbors (except the pixels at the edge of the image) as shown in Figure 2(a). Different from the common pixels, the noise pixels usually have huge disparities with most of their neighbors. In previous articles, the noises usually are processed by filter, but the filter is only suitable for specific images which will lead to the deterioration of segmentation accuracy for edge blur. A noise reduction mechanism is proposed in this section which integrates into AFSA and achieves the identification and procession of noises. Gaussian noise is chosen to test the antinoise ability of HAFSA.


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)

Noise pixel and neighbor regions.
© Copyright Policy
Related In: Results  -  Collection

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

fig2: Noise pixel and neighbor regions.
Mentions: Most noises are Gaussian noise and salt and pepper noise in medical images. Noises generally locate in the same cluster or boundary of several different clusters. The gray image is mostly like a grid network where each pixel possesses 8 neighbors (except the pixels at the edge of the image) as shown in Figure 2(a). Different from the common pixels, the noise pixels usually have huge disparities with most of their neighbors. In previous articles, the noises usually are processed by filter, but the filter is only suitable for specific images which will lead to the deterioration of segmentation accuracy for edge blur. A noise reduction mechanism is proposed in this section which integrates into AFSA and achieves the identification and procession of noises. Gaussian noise is chosen to test the antinoise ability of HAFSA.

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