TY - JFULL AU - Amir Moslemi and Amir Movafeghi and Shahab Moradi PY - 2015/11/ TI - CT Medical Images Denoising Based on New Wavelet Thresholding Compared with Curvelet and Contourlet T2 - International Journal of Computer and Information Engineering SP - 2166 EP - 2172 VL - 9 SN - 1307-6892 UR - https://publications.waset.org/pdf/10002440 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 106, 2015 N2 - One of the most important challenging factors in medical images is nominated as noise. Image denoising refers to the improvement of a digital medical image that has been infected by Additive White Gaussian Noise (AWGN). The digital medical image or video can be affected by different types of noises. They are impulse noise, Poisson noise and AWGN. Computed tomography (CT) images are subjects to low quality due to the noise. Quality of CT images is dependent on absorbed dose to patients directly in such a way that increase in absorbed radiation, consequently absorbed dose to patients (ADP), enhances the CT images quality. In this manner, noise reduction techniques on purpose of images quality enhancement exposing no excess radiation to patients is one the challenging problems for CT images processing. In this work, noise reduction in CT images was performed using two different directional 2 dimensional (2D) transformations; i.e., Curvelet and Contourlet and Discrete Wavelet Transform (DWT) thresholding methods of BayesShrink and AdaptShrink, compared to each other and we proposed a new threshold in wavelet domain for not only noise reduction but also edge retaining, consequently the proposed method retains the modified coefficients significantly that result good visual quality. Data evaluations were accomplished by using two criterions; namely, peak signal to noise ratio (PSNR) and Structure similarity (Ssim). ER -