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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

HAFSA.
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alg1: HAFSA.

Mentions: HAFSA is proposed in this section by introducing Metropolis criterion and noise reduction mechanism, based on AFSA. HAFSA inherits the computing framework of AFSA which has the advantages of strong optimization ability and less easiness of falling into the local optimum. By introducing Metropolis criterion, HAFSA's convergence speed has been accelerated and segmentation accuracy has been improved. The proposed noise reduction mechanism makes full use of AFSA's cluster result, which can improve the noise identification ability of HAFSA; meanwhile it has fewer computations than other noise reduction mechanisms. HAFSA is shown in Algorithm 1.


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)

HAFSA.
© Copyright Policy
Related In: Results  -  Collection

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

alg1: HAFSA.
Mentions: HAFSA is proposed in this section by introducing Metropolis criterion and noise reduction mechanism, based on AFSA. HAFSA inherits the computing framework of AFSA which has the advantages of strong optimization ability and less easiness of falling into the local optimum. By introducing Metropolis criterion, HAFSA's convergence speed has been accelerated and segmentation accuracy has been improved. The proposed noise reduction mechanism makes full use of AFSA's cluster result, which can improve the noise identification ability of HAFSA; meanwhile it has fewer computations than other noise reduction mechanisms. HAFSA is shown in Algorithm 1.

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