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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 results of MRI: (a) MRI; (b) FCM on MRI; (c) SFCM on MRI; (d) HAFSA on MRI; (e) WM (standard segmentation); (f) WM (FCM); (g) WM (SFCM); (h) WM (HAFSA); (i) GM (standard segmentation); (j) GM (FCM); (k) GM (SFCM); (l) GM (HAFSA); (m) CSF (standard segmentation); (n) CSF (FCM); (o) CSF (SFCM); (p) CSF (HAFSA).
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fig5: Segmentation results of MRI: (a) MRI; (b) FCM on MRI; (c) SFCM on MRI; (d) HAFSA on MRI; (e) WM (standard segmentation); (f) WM (FCM); (g) WM (SFCM); (h) WM (HAFSA); (i) GM (standard segmentation); (j) GM (FCM); (k) GM (SFCM); (l) GM (HAFSA); (m) CSF (standard segmentation); (n) CSF (FCM); (o) CSF (SFCM); (p) CSF (HAFSA).

Mentions: The second experiment is on MRI, introducing MRI with 5% salt and pepper noise to verify the segmentation effectiveness of HAFSA in the real world. MRI includes 4 parts: the white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and background. Hence, MRI can be divided into 4 clusters [10]. The first column of Figure 5 presents the original MRI, standard segmentation of WM, standard segmentation of GM, and standard segmentation of CSF; from the second column to the fourth column are the segmentation results of FCM, SFCM, and HAFSA, respectively. Observing the first row of Figure 5, the boundaries segmented by FCM and SFCM are fuzzy but HAFSA is much clearer. For further comparison, the segmentation results of 3 algorithms have been divided into 3 target regions which are the white matter (WM), the gray matter (GM), and the cerebrospinal fluid (CSF), as shown in the second to the fourth rows of Figure 5. The basic FCM algorithm and the SFCM algorithm obtain the inferior effectiveness of segmentation whose edges are in the 3 target regions with a lot of trivial and rough areas, and the details of the target regions cannot be distinguished clearly. The proposed HAFSA can restore the original details even under complicated noise environment. The segmentation figures generated by HAFSA usually have clear outline, complete targets, and the maximum similarity with the standard segmentations which are divided manually by experienced experts.


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 results of MRI: (a) MRI; (b) FCM on MRI; (c) SFCM on MRI; (d) HAFSA on MRI; (e) WM (standard segmentation); (f) WM (FCM); (g) WM (SFCM); (h) WM (HAFSA); (i) GM (standard segmentation); (j) GM (FCM); (k) GM (SFCM); (l) GM (HAFSA); (m) CSF (standard segmentation); (n) CSF (FCM); (o) CSF (SFCM); (p) CSF (HAFSA).
© Copyright Policy
Related In: Results  -  Collection

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

fig5: Segmentation results of MRI: (a) MRI; (b) FCM on MRI; (c) SFCM on MRI; (d) HAFSA on MRI; (e) WM (standard segmentation); (f) WM (FCM); (g) WM (SFCM); (h) WM (HAFSA); (i) GM (standard segmentation); (j) GM (FCM); (k) GM (SFCM); (l) GM (HAFSA); (m) CSF (standard segmentation); (n) CSF (FCM); (o) CSF (SFCM); (p) CSF (HAFSA).
Mentions: The second experiment is on MRI, introducing MRI with 5% salt and pepper noise to verify the segmentation effectiveness of HAFSA in the real world. MRI includes 4 parts: the white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and background. Hence, MRI can be divided into 4 clusters [10]. The first column of Figure 5 presents the original MRI, standard segmentation of WM, standard segmentation of GM, and standard segmentation of CSF; from the second column to the fourth column are the segmentation results of FCM, SFCM, and HAFSA, respectively. Observing the first row of Figure 5, the boundaries segmented by FCM and SFCM are fuzzy but HAFSA is much clearer. For further comparison, the segmentation results of 3 algorithms have been divided into 3 target regions which are the white matter (WM), the gray matter (GM), and the cerebrospinal fluid (CSF), as shown in the second to the fourth rows of Figure 5. The basic FCM algorithm and the SFCM algorithm obtain the inferior effectiveness of segmentation whose edges are in the 3 target regions with a lot of trivial and rough areas, and the details of the target regions cannot be distinguished clearly. The proposed HAFSA can restore the original details even under complicated noise environment. The segmentation figures generated by HAFSA usually have clear outline, complete targets, and the maximum similarity with the standard segmentations which are divided manually by experienced experts.

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