Adaptive denoising of 3D volumetric MR images using local variance based estimator

Das, P and Pal, C and Acharyya, Amit and et al, . (2020) Adaptive denoising of 3D volumetric MR images using local variance based estimator. Biomedical Signal Processing and Control, 59. ISSN 1746-8094

Full text not available from this repository. (Request a copy)


Preservation of the anatomical structures during denoising of medical images is a very significant and challenging operation. Corruption of magnetic resonance image (MRI) by Rician noise is inherent to the acquisition process, affecting diagnosis. In this study, we present a novel filtering methodology that removes Rician noise from MRI by estimating the local noise variance, which drives the diffusion process of the filter. In our methodology, the adaptation of statistical edge stopping function captivates the preservation condition of the anatomical structure of the MRI images. The results obtained on synthetic/simulated MRI datasets (3D) and real MRI datasets confirm the accuracy and robustness of the proposed methodology. Compared to the benchmark approaches like BM4D, LTA3D, RNOLMMSE, ROLMMSE, MNL-tSVD and PRINLM3D, the optimized way of choosing the edge stopping functions, the automatic adjustment of the filtering coefficients and variance based local noise estimation technique lead to a qualitative and quantitative robust estimation performance, in case of both simulated and real datasets.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Acharyya, Amit
Item Type: Article
Uncontrolled Keywords: Adaptive diffusion-based MRI filtering (ADMF3D), Denoising, Magnetic resonance imaging (MRI)Noise estimation, Rician noise, Indexed in Scopus
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: Team Library
Date Deposited: 16 Mar 2020 05:24
Last Modified: 16 Mar 2020 05:24
Publisher URL:
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 7526 Statistics for this ePrint Item