In view of the speckle noise in the synthetic aperture radar (SAR) images,and based on the Contourlet′s advantages of multiscale,localization,directionality,and anisotropy,a new SAR image fusion segmentation algorithm based on the persistence and clustering in the Contourlet domain is proposed. The algorithm captures the persistence and clustering of the Contourlet transform,which is modeled by hidden Markov tree (HMT) and Markov random field (MRF),respectively. Then,these two models are fused by fuzzy logic,resulting in a Contourlet domain HMT-MRF fusion model. Finally,the maximum a posterior (MAP) segmentation equation for the new fusion model is deduced. The algorithm is used to emulate the real SAR images. Simulation results and analysis indicate that the proposed algorithm effectively reduces the influence of multiplicative speckle noise,improves the segmentation accuracy and provides a better visual quality for SAR images over the algorithms based on HMT-MRF in the wavelet domain,HMT and MRF in the Contourlet domain,respectly.