Comparative Analysis of Curvelet Based Techniques for Denoising of Computed Tomography Images

The purpose of this paper is to carry out the performance assessment of the noise reduction methods on the brain Computed Tomography (CT) images. In particular, total three multiscale geometric curvelet based denoising methods are evaluated and compared with wavelet based methods. The experimental results show that cycle spinning based curvelet method outperforms other curvelet based methods as well as the wavelet based methods. This comparative study is focused not only on the noise suppression but also on fine details and edge preservation. The quality assessment parameters used in this paper are Signal-to-noise-ratio (SNR), Peak-signal-to-noise-ratio (PSNR), Universal Quality Index (UQI) and Edge keeping index (EKI).