sensitivity to the initial value and poor anti-noise performance are two important factors affecting fuzzy c-mean (FCM) clustering in image segmentation. In this study

image segmentation based on gene expression programming (GEP) and spatial fuzzy clustering is proposed to solve the two problems. GEP is a novel adaptive evolutionary algorithm that can solve the problems by simulating biological gene structure and genetic evolution. The performance of GEP is excellent

and it currently has a few applications in remote-sensing image segmentation. The standard FCM only deals with the gray-level information of pixels. However

the pixels on an image are highly correlated

and the pixels within the neighborhood have almost the same data characteristics. Therefore

the spatial relationships among adjacent pixels must be considered an important feature in image segmentation. The GEP owns a unique structure

a more flexible coding method

and a richer genetic operator

which make it a better search method compared with other methods. In the proposed method

the GEP algorithm is introduced for the initial segmentation at the first stage. The clustering centers are encoded into chromosomes as the action objects in GEP. With genetic operations

namely

selection

recombination

and mutation

chromosomes that represent individuals will evolve to the next generation. Fitness function is used to evaluate each individual

which is set as the reciprocal of the objective function in the FCM in this study. After a certain number of evolutions

the individual with the highest fitness value will then be kept as the initial solution. At the second stage

spatial function is introduced to reduce the adverse effects of noise points on the image segmentation. With spatial function values of pixels included

the membership function is redefined. The overall process of spatial fuzzy clustering is the same as that of FCM; however

the initial value is from the result of the first stage. The segmentation experiments on noisy synthetic image and noisy Berkeley images show that the performances of the proposed method in index partition coefficient (VPC)

clustering entropy (VPE)

and peak signal-to-noise ratio (PSNR) are much better than those of the other two classical clustering segmentation algorithms. The average VPC values are 0.062 4 and 0.061 1 higher than those of the classical algorithms. The average VPE decreases by 0.117 0 and 0.101 1

and the average PSNR increases by approximately 13.312 1 and 3.308 4. Although the run time required for this method increases

the number of iterations required for the solution is greatly reduced. The segmentation experiments of six images in the Berkeley image library show that the VPC values of the proposed method are all above 0.93

which is 0.157 6 and 0.013 3 higher than the values of the two comparison methods. The mean value of PSNR increases by 2.896 3 and 1.934 4

and the VPE values are in the vicinity of 0.1 and are lower than the comparison methods. On the multi-target segmentation experiment

the segmentation performance of the three methods decreases with an increase in the number of clusters. However

the performance curve of the proposed method is the smoothest

and the results are the least affected by the number of clusters. The proposed method has strong capabilities of anti-noise

high segmentation accuracy

and stability. This method is suitable for the segmentation scenario

in which highly accurate results are required and time requirements are not high.