3D MRI denoising using rough set theory and kernel embedding method

Abstract

In this paper, we have presented a manifold embedding based method for denoising volumetric MRI data. The proposed method via kernel mapping tries to find linearity among data in the projection/ feature space. Prior to kernel mapping, a Rough Set Theory (RST) based clustering technique has been used with extension to volumetric data. RST clustering method groups similar voxels (3D cubes) using class and edge information. The basis vector representation of each cluster is then explored in the Kernel space via Principal Component Analysis (known as KPCA). The work has been compared with state-of-the-art methods under various measures for synthetic and real databases.