Adding spatial penalty to fuzzy C-means (FCM) model is an important way to reduce the influence of noise in image segmentation. However, these improved algorithms easily cause segmentation failures when the image has the characteristics of unequal cluster sizes. Besides, they often fall into local optimal solutions if the initial cluster centers are improper. This paper presents a noise robust hybrid algorithm for segmenting image with unequal cluster sizes based on chaotic crow search algorithm and improved fuzzy c-means to overcome the above defects. Firstly, each size of clusters is integrated into the objective function of noise detecting fuzzy c-means algorithm (NDFCM), which can reduces the contribution of larger clusters to objective function and then the new membership degree and cluster centers are deduced. Secondly, a new expression called compactness, representing the pixel distribution of each cluster, is introduced into the iteration process of clustering. Thirdly, we use two- paths to seek the optimal solutions in each step of iteration: one path is produced by the chaotic crow search algorithm and the other is originated by gradient method. Furthermore, the better solutions of the two-paths go to next generation until the end of the iteration. Finally, the experiments on the synthetic and non–destructive testing (NDT) images show that the proposed algorithm behaves well in noise robustness and segmentation performance.
PalN.R. and PalS.K., A review on image segmentation techniques, Pattern Recognition26(9) (1993), 1277–1294.
2.
QamarU., A dissimilarity measure based fuzzy c-means (FCM) clustering algorithm, Journal of Intelligent & Fuzzy Systems26 (2014), 229–238.
3.
SealA., KarlekarA., KrejcarO. and Gonzalo-MartinC., Fuzzy c-means clustering using Jeffreys-divergence based similarity measure, Applied Soft Computing88 (2020), 106016.
4.
ZhaoF., FanJ. and LiuH., Optimal-selection-based suppressed fuzzy c-means clustering algorithm with self-tuning non local spatial information for image segmentation, Expert Systems Applications41 (2014), 4083–4093.
5.
VermaH., AgrawalR.K. and SharanA., An improved intuitionistic fuzzy c-means clustering algorithm incorporating local information for brain image segmentation, Applied Soft Computing46 (2016), 543–557.
6.
WanL., ZhangT., XiangY. and YouH., A robust fuzzy C-means algorithm based on Bayesian nonlocal spatial information for SAR image segmentation, IEEE Journal Selected Topics Applied Earth Observations and Remote Sensing11(3) (2018), 896–906.
7.
JiJ. and WangK.L., A robust nonlocal fuzzy clustering algorithm with between-cluster separation measure for SAR image segmentation, IEEE Journal Selected Topics Applied Earth Observations and Remote Sensing7(12) (2014), 4929–4936.
8.
BezdekJ.C., A convergence theorem for the fuzzy ISODATA clustering algorithms, IEEE Transactions on Pattern Analysis and Machine Intelligence2(1) (1980), 1–8.
9.
AhmedM.N., YamanyS.M., MohamedN., FaragA.A. and MoriartyT., A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data, IEEE Transactions on Medical Imaging21 (2002), 193–199.
10.
ChenS. and ZhangD., Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure, IEEE Transactions on Systems Man and Cybernetics Part B: Cybernetics34 (2004), 1907–1916.
11.
SzilagyiL., BenyoZ., SzilagyiS.M. and AdamH.S., MR brain image segmentation using an enhanced fuzzy c-means algorithm. In, Proceedings of the 25th annual international conference of the IEEE engineering in medicine and biology society1 (2003), 17–21.
12.
CaiW., ChenS. and ZhangD., Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation, Pattern Recognition40(3) (2007), 825–838.
13.
KrinidisS. and ChatzisV., A robust fuzzy local information C-means clustering algorithm, IEEE Transactions on Image Processing19(5) (2010), 1328–1337.
14.
GongM., ZhouZ. and MaJ., Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering, IEEE Transactions on Image Processing21(4) (2012), 2141–2151.
15.
LiF. and QinJ., Robust fuzzy local information and}}-norm distance-based image segmentation method, Iet Image Processing11(4) (2017), 217–226.
16.
WuC. and YangX., Robust credibilistic fuzzy local information clustering with spatial information constraints, Digital Signal Processing97 (2020), 102615.
17.
ZhangH., WangQ., ShiW. and HaoM., A novel adaptive fuzzy local information C-means clustering algorithm for remotely sensed imagery classification, IEEE Transactions on Geoscience & Remote Sensing55(9) (2017), 5057–5068.
18.
FengJ., JiaoL.C., ZhangX., GongM. and SunT., Robust non-local fuzzy c-means algorithm with edge preservation for SAR image segmentation, Signal Processing91(4) (2011), 988–999.
19.
ZhaoF., JiaoL., LiuH. and GaoX., A novel fuzzy clustering algorithm with non-local adaptive spatial constraint for image segmentation, Signal Processing93(2) (2013), 487–499.
20.
MukhopadhyayA. and MaulikU., A multiobjective approach to MR brain image segmentation, Applied Soft Computing11(1) (2011), 872–880.
21.
DingY. and FuX., Kernel-based fuzzy c-means clustering algorithm based on genetic algorithm, Neurocomputing188 (2016), 233–238.
22.
BenaichoucheA.N., OulhadjH. and SiarryP., Improved spatial fuzzy c-means clustering for image segmentation using PSO initialization, Mahalanobis distance and post-segmentation correction, Digital Signal Processing23(5) (2013), 1390–1400.
23.
ZhangM., JiaoL., MaW., MaJ. and GongM., Multi-objective evolutionary fuzzy clustering for image segmentation with MOEA/D, Applied Soft Computing48 (2016), 621–637.
24.
NiknimT. and AmiriB., An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis, Applied Soft Computing10(1) (2009), 183–197.
25.
BensaidA.M., HallL.O., BezdekJ.C. and ClarkeL.P., Partially supervised clustering for image segmentation, Pattern Recognition29(5) (1996), 859–871.
26.
NoordamJ.C., van den BroekW.H.A.M. and BuydensL.M.C., Multivariate image segmentation with cluster size insensitive Fuzzy C-means, Chemometrics and Intelligent Laboratory Systems64 (2002), 65–78.
27.
LinP.L., HuangP.W., KuoC.H. and LaiY.H., A size-insensitive integrity-based fuzzy c-means method for data clustering, Pattern Recognition47(5) (2014), 2042–2056.
28.
LiuY., HouT. and LiuF., Improving fuzzy c -means method for unbalanced dataset, Electronics Letters51(23) (2015), 1880–1882.
29.
AskarzadehA., A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm, Computers and Structures169 (2016), 1–12.
30.
GuoF.F., WangX.X. and ShenJ., Adaptive fuzzy c-means algorithm based on local noise detecting for image segmentation, IET Image Processing10(4) (2016), 272–279.
31.
LiuX., HeY., LuF., WuC. and CaiX., Chaotic crow search algorithm based on differential evolution strategy for solving discount 0-1 knapsack problem, Journal of Computer Applications38(1) (2018), 137–145. (in Chinese).
32.
AlatasB., Chaotic bee colony algorithms for global numerical optimization, Expert Systems and Applications37(8) (2010), 5682–5687.
33.
LiC., ZhouJ., KouP. and XiaoJ., A novel chaotic particle swarm optimization based fuzzy clustering algorithm, Neurocomputing83 (2012), 98–109.
34.
ZalikK.R., Cluster validity index for estimation of fuzzy clusters of different sizes and densities, Pattern Recognition43(10) (2010), 3374–3390.
35.
ZalikK.R. and ZalikB., Validity index for clusters of different sizes and densities, Pattern Recognition Letters32(2) (2010), 221–234.
36.
TavazoeiM.S. and HaeriM., Comparison of different one-dimensional maps as chaotic search pattern in chaos optimization algorithms, Applied Mathematics and Computation187 (2007), 1076–1085.
37.
HanH., WangW.Y. and MaoB.H., Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning, Advances in Intelligent Computing (2005), 878–887.
38.
DengX., ZhanD.C. and ZhouZ.H., Supervised nonlinear dimensionality reduction for visualization and classification, IEEE Transactions on Systems Man and Cybernetics Part B: Cybernetics35(6) (2005), 1098–1107.
