Liver cancer is the abnormal growth of cells in the liver. The survival of patients is strictly related to the phase of the detected cancer. Several imaging modalities have been proposed for Liver’s cancer detection and characterization, including ultrasonography, computed tomography (CT), and positron emission tomography (PET). Although computed tomography is commonly the most preferred, low dose CT images have low contrast and high Gaussian noise. This paper reviews CT image denoising algorithms which are based on wavelet, Curvelets and contourlets transforms and evaluates them based on many criteria. Discussions and interpretations for these methods are given in detail to evaluate their performances. Simulation results show that the Curvelets transform provides better PSNR, SNR and higher NCC values.