In this paper, an unsupervised multiresolution image segmentation algorithm is put forward, which combines interscale and intrascale Markov random field and fuzzy c-means clustering with spatial constraints. In the initial label determination of wavelet coefficient phase, the statistical distribution property of wavelet coefficients is characterized by Gaussian mixture model, the properties of intrascale clustering and interscale persistence of wavelet coefficients are captured by Markov prior probability model. According to maximum a posterior rule, the initial label of wavelet coefficient from coarse to fine scale is determined. In the image segmentation phase, in order to overcome the shortcomings of conventional fuzzy c-means clustering, such as being sensitive to noise and lacking of spatial constraints, we construct the novel fuzzy c-means objective function based on the property of intrascale clustering and interscale persistence of wavelet coefficients, taking advantage of Lagrange multipliers, the improved objective function with spatial constraints is optimized, the final label of wavelet coefficient is determined by iteratively updating the membership degree and cluster centers. The experimental results on real magnetic resonance image and peppers image with noise show that the proposed algorithm obtains much better segmentation results, such as accurately differentiating different regions and being immune to noise.