A maximum likelihood estimation method for denoising magnitude MRI using restricted local neighborhood
Faculty of Sciences. Physics
Faculty of Pharmaceutical, Biomedical and Veterinary Sciences . Biomedical Sciences
Engineering sciences. Technology
Proceedings of SPIE
, p. 79624U,1-79624U,6
University of Antwerp
n this paper, we propose a method to denoise magnitude Magnetic Resonance (MR) images based on the maximum likelihood (ML) estimation method using a restricted local neighborhood. Conventionally, methods that estimate the true, underlying signal from a local neighborhood assume this signal to be constant within that neighborhood. However, this assumption is not always valid and, as a result, the edges in the image will be blurred and fine structures will be destroyed. As a solution to this problem, we put forward the concept of using a restricted local neighborhood where the true intensity for each noisy pixel is estimated from a set of selected neighboring pixels. To this end, a reference image is created from the noisy image using a recently proposed non local means algorithm. This reference image is used as a prior for further noise reduction. A scheme is developed to locally select an appropriate subset of pixels from which the underlying signal is estimated. Experimental results based on Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Matrix (SSIM) and Bhattacharrya coefficient from synthetic and real MRI demonstrate the superior performance of the proposed method over other state of the art methods.