AN EFFICIENT RIPPLET-BASED SHRINKAGE TECHNIQUE FOR MR IMAGE RESTORATION

Abstract

In this paper a new ripplet-based shrinkage technique is used to suppress noise from Magnetic Resonance Imaging (MRI). The propitious properties of ripplet transform such as anisotropy, high directionality, good localization, and high-energy compaction make the proposed method efficient and feature preserving when compared to other transforms. Ripplet transform provides efficient representation of edges in images with a higher potential for image processing applications such as image restoration, compression, and de-noising. The proposed method implies a new nonlinear ripplet-based shrinkage technique to extract the spatial and frequency information from MRI corrupted by noise. The choice of this new shrinkage technique is due to its simplicity, versatility, and its efficiency in removing noise from homogenous regions and those regions with singularities, when compared to the existing filtering techniques. Experiments were conducted on several diffusion weighed images and anatomical images. The results show that the proposed de-noising technique shows competitive performance compared to the current state-of-art methods. Qualitative validation was performed based on several quality metrics and profound improvement over existing methods was obtained. Higher values of Peak Signal to Noise Ratio (PSNR), Correlation Coefficient (CC), mean structural similarity index (MSSIM), and lower values of Root Mean Square Error (RMSE) and computational time were obtained for the proposed ripplet-based shrinkage technique when compared to the existing ones.