Denoising of 3D magnetic resonance images using non-local PCA and Transform-Domain Filter

  • Laraib Kanwal Lahore Garrison University
Keywords: MRI, PCA, Denoising, BM4D, PRI-NL-PCA, Wiener filter.

Abstract

The Magnetic Resonance Imaging (MRI) technology
used in clinical diagnosis demands high Peak Signal-to-Noise ratio
(PSNR) and improved resolution for accurate analysis and treatment
monitoring. However, MRI data is often corrupted by random noise
which degrades the quality of Magnetic Resonance (MR) images.
Denoising is a paramount challenge as removing noise causes
reduction in the fine details of MRI images. We have developed a
novel algorithm which employs Principal Component Analysis
(PCA) decomposition and Wiener filtering. We have proposed a two
stage approach. In first stage, non-local PCA thresholding is applied
on noisy image and second stage uses Wiener filter over this filtered
image. Our algorithm is implemented using MATLAB and
performance is measured via PSNR. The proposed approach has
also been compared with related state-of-art methods. Moreover, we
present both qualitative and quantitative results which prove that
proposed algorithm gives superior denoising performance.

Published
2017-03-31
How to Cite
Laraib Kanwal. (2017). Denoising of 3D magnetic resonance images using non-local PCA and Transform-Domain Filter. Lahore Garrison University Research Journal of Computer Science and Information Technology, 1(1), 69-82. https://doi.org/10.54692/lgurjcsit.2017.01018
Section
Articles