Suppose each pixel of an image is a random variable under some kind of probability distribution

according to the Bayes theorem

the segmentation of the original images is equivalent to their maximum a posteriori probability estimation. In this framework

we proposed an improved image segmentation algorithm based on Graph Cuts. The construction of the original Graph Cuts model is improved in two aspects. First

fuzzy C-means clustering is introduced into the energy function of data restriction. With the help of fuzzy clustering method

the energy function’s performance of constringency is improved. Second

nonparametric method is used to estimate the statistical distribution of the image

which work as the prior probability used in image segmentation. With the presented method

the results of segmentation are guaranteed to be smooth locally. Since the nonparametric estimation is directly evaluated from the samples

and is suitable for situations of small samples and variable distribution functions

the applicability of our algorithm is extended. Experimental results have shown that the proposed algorithm has good performance on segmenting remote sensing images and medical images.