In this paper, we study unsupervised image segmentation using wavelet-domain hidden Markov models (HMMs). We first review recent supervised Bayesian image segmentation algorithms using wavelet-domain HMMs. Then, a new unsupervised segmentation approach is developed by capturing the likelihood disparity of different texture features with respect to wavelet-domain HMMs. The K-mean clustering is used to convert the unsupervised segmentation problem into a self-supervised process by identifying the reliable training samples. The simulation results on synthetic mosaics and real images show that the proposed unsupervised segmentation algorithm can achieve high classification accuracy that is close to the supervised one.