Considering the normal contourlet transform only decomposed the low frequency coefficients of the signals, and ignored the high frequency coefficients, a novel complex contourlet packet transform is constructed by combining the analytic dual-tree complex wavelet packet transform and nonsubsampled directional filter banks. Then a complex contourlet packet image denoising algorithm based on neighbouring thresholding classification is proposed. The new transform has good characteristics of multiresolution, localization, directionality and anisotropy, as well as translation invariance. Furthermore, it has more abundant direction components. The experimental result shows that the complex contourlet packet transform can restrain Gibbs-like artificial around edges in the course of denoising, and preserve more details and textures of the images efficiently. The PSNR and the visual quality of this algorithm are also superior to the traditional methods.