Medical image denoising using adaptive fusion of curvelet transform and total variation

In medical images noise and artifacts are introduced due to the acquisition techniques and systems. Due to the noise present in the medical images, experts may not be able to draw correct and useful information from the images. The paper proposes a noise reduction method for both computed tomography (CT) and magnetic resonance imaging (MRI) which fuses the images (i) denoised by total variation (TV) method, (ii) denoised by curvelet based method and (iii) the edge information, where edge information is extracted from the noise residual of TV method by processing it through curvelet transform. The performance of the proposed method is evaluated on real brain CT and MRI images and results show significant improvement not only in noise suppression but also in edge preservation.