Comparative analysis of Median filter family for Removing High-Density Noise in Magnetic Resonance Images

  • Nada Jasim Habeeb Middle Technical University, Baghdad, Iraq
Keywords: impulse noise, image de-noising, median filter, adaptive median filter, PSNR, SSIM, Beta metric

Abstract

Magnetic Resonance Imaging (MRI) is a medical indicative test utilized for taking images of the tissue points of interest of the human body. During image acquisition, MRI images can be damaged by many noise signals such as impulse noise. One reason for this noise may be a sharp or sudden disturbance in the image signal. The removal of impulse noise is one of the real difficulties. As of late, numerous image de-noising methods were produced for removing the impulse noise from images. Comparative analysis of known and modern methods of median filter family is presented in this paper. These filters can be categorized as follows: Standard Median Filter; Adaptive Median Filter; Progressive Switching Median Filter; Noise Adaptive Fuzzy Switching Median Filter; and Different Applied Median Filter. The de-noising technique performance for each one is evaluated and compared using Peak Signal Noise Ratio, Structural Similarity index Metric, and Beta metric as quantitative metrics.  The experimental results showed that the latest de-noising technique, Different Applied Median Filter (DAMF), produced better results in removing impulse noise compared with the other de-noising techniques. However, this filter produced de-noised image with nonlinear edges in high-density noise. As a result, noise removal from images is one of the low-level images processing which is considered as a first step in many image applications. Therefore, the efficiency of any image processed depends on the efficiency of noise removal technique.

Published
2019-10-28
How to Cite
Habeeb, N. J. (2019). Comparative analysis of Median filter family for Removing High-Density Noise in Magnetic Resonance Images. Iraqi Journal of Science, 60(10), 2246-2256. https://doi.org/10.24996/ijs.2019.60.10.19
Section
Computer Science