Performance evaluation of curvelet and wavelet based denoising methods on brain Computed Tomography images

This paper presents the evaluation of the effect of noise reduction techniques on the brain Computed Tomography (CT) images. In particular, multiscale geometric denoising methods based on curvelet transform are used and compared with wavelet based methods. The simulated results show that cycle spinning based curvelet transform method outperforms the wavelet based methods not only for the suppression of noise but also for preservation of fine details and edges and allow the use of a low dose brain CT images. However it generates some extra edges in homogenous regions of the image. The quality assessment parameters used in this paper are Mean square error (MSE), Peak-signal-to noise ratio (PSNR) and Edge keeping index (EKI).