We propose an improved(FCM)approach for interactive image segmentation to solve the problem that the objective contour is easily influenced by natural texture.Users interactively input seed points as clusters centers to get the main feature differences between foreground and background;a global spatial similarity measure model and Gabor energy filters are introduced into the distance metric to measure the similarity between all the pixels and the cluster centers.Edge density is introduced into the definition of the weight factor

according to image features

the ratio of the texture feature and the color feature in the feature space for pixels which can be adaptively calculated

making the feature we calculated more accurately reflect the essential attributes of the image.In this paper

experiments have been carried out on images with natural texture background

and two performance measures were used to compare the proposed algorithm with the random walker(RW)algorithm.It is shown that the accuracy of the proposed method surpasses FCM and RW methods.