Due to the image noise and boundary uncertainty, the noise resistance and accuracy of image segmentation algorithm is a challenging task. Two improvement fuzzy clustering algorithms for image segmentation are proposed. The proposed algorithms for image segmentation act as the following two steps. The first step is detecting the probability of every central pixel being a noise point adaptively based on the grey levels in its local information. The detecting results, playing the roles of denoising and detail information, are used to construct a new image, and then two novel segmentation algorithms based on fuzzy clustering are proposed. The second step is detecting the potentially misclassified pixels and refining the segmentation results by correcting the errors of clustering for improving the segmentation accuracy and visual effects. The obtained segmentation algorithms are carried out on synthetic image, Berkeley images and other real images in different noise levels. The results show that the proposed algorithm has advantages of segmentation accuracy and adjusted rand index compared with the others fuzzy clustering algorithms, and the segmentation results have clear contour and better visual effects.