Evaluating denoising performances of basic filters in the detection of microcalcifications on mammogram images

Franca Oyiwoja Okoh * and John Actor Ocheje

Department of Pure and Applied Physics, Federal University Wukari, PMB 1020 Wukari-Taraba State, Nigeria.
 
Research Article
International Journal of Science and Research Archive, 2023, 09(02), 201–210.
Article DOI: 10.30574/ijsra.2023.9.2.0544
Publication history: 
Received on 01 June 2023; revised on 09 July 2023; accepted on 12 July 2023
 
Abstract: 
This research evaluates the denoising abilities of some image-processing filters used in facilitating the early detection of microcalcifications in breast tissues. The mean, median and Gaussian filters were employed to denoise mammogram images of microcalcification breast phantoms of various densities. The performances of the filters were assessed by evaluating the mean squared error (MSE), peak signal-to-noise ratio (PSNR), and signal-to-noise ratio (SNR). All experiments were carried out on MATLAB R2020a platform. The results revealed that the Gaussian filter recorded optimal performance in denoising images with all 3 types of added noises compared to the mean and median filters. The PSNR value of the heterogeneous phantom (PVAL/H) was superior to those of the less dense (PVAL/E), dense (PVAL), and extremely dense (PVAL/G) phantoms for all the tested filters. The results of this work agree with the high contrast recorded by the original image of PVAL/H.
 
Keywords: 
Breast cancer; Mean filter; Median filter; Gaussian filter; MSE; SNR; PSNR
 
Full text article in PDF: 
 
Copyright information: 

Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0