A texture image retrieval approach based on Dual-tree Contourlet (DT-Contourlet) transform is proposed. DT-Contourlet is constructed by the pyramidal dual-tree directional filter bank. It maintains the flexibility direction selectivity of the contourlet transform, and overcomes the limitation of traditional contourlet which namely lacks of shift invariance．According to the statistical characteristics of DT-Contourlet coefficients, each subband is modeled by generalized Gaussian distribution, and the moment estimator is used to extract model parameters. Finally, the texture image retrieval is implemented by computing the Kullback–Leibler Distance (KLD) between estimated models. To evaluate the retrieval performance, texture database including 800 texture images from Bradatz album is created. Experimental results indicate that, compared with traditional approaches based on wavelet and contourlet, the proposed approach improves average retrieval rate with 10.46% and 6.94% respectively and retains high computational efficiency.