Motivated by the need for a framework dedicated to multi modal images processing and analysis, as well as Wavelet and Contourlet transforms properties, we developed a WCHMT (Wavelet-Contourlet Hidden Markov Tree) as a quadtree-structured probabilistic graph that captures the statistical properties of the whole observed data. This combining strategy was applied to exploit advantages of both Wavelet and Contourlet transforms. The proposed framework has been first evaluated in this work for mono modal PET image denoising. Simulated images of the IEC phantom in a PET Philips Allegro scanner were obtained through GATE Monte Carlo simulations. The results of denoising using the proposed WCHMT with respect to local adaptive Bishrink wavelet denoising currently considered as the state-of-the-art filter in PET imaging, are promising. High increases of the SNR were obtained with both methods. However, denoising performed through WCHMT led to lower blurring and quantitative bias in both absolute intensities and contrast recovery. Future work will investigate the potential of the WCHMT for multi modal (PET/CT, PET/MR) images segmentation, deconvolution and filtering.