The spatial generalized fuzzy c-means clustering algorithm (GFCM_S) is a popular technique for image segmentation

but it is not so effective when the image has the features of unequal cluster sizes or the initial cluster centers we choose are improper. In this paper

for solving the above shortcomings of GFCM_S

a novel algorithm incorporating chaos optimization and improved fuzzy c-means (CIGFCM_S) is proposed. Firstly

each size of clusters is integrated into the objective function of GFCM_S so as to equalize the contribution of larger and smaller clusters to the objective function. Secondly

the iteratively membership degree and cluster centers are deduced by the Lagrange multiplier method. Thirdly

a new iterative strategy is used to seek the optimal solutions. In detail

the optimal solutions of next generation are searched by two-paths

one path originates chaos optimization and the other is obtained by updating membership degree and cluster centers on the basis of current optimal solutions

and then the better solutions go to next generation until the end. Lastly

the non-destructive testing (NDT) images with the characters of unequal cluster sizes are used for experiments

the results show that the proposed algorithm has better segmentation accuracy and visual effects.