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DOI: 10.14569/IJACSA.2020.0111178
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A Repeated Median Filtering Method for Denoising Mammogram Images

Author 1: Hussain AlSalman

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 11, 2020.

  • Abstract and Keywords
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Abstract: In the medical field, mammogram analysis is one of the most important breast cancer detection procedures and early diagnosis. During the image acquisition process of mammograms, the acquired images may be contained some noises due to the change of illumination and sensor error. Hence, it is necessary to remove these noises without affecting the edges and fine details, achieving an effective diagnosis of beast images. In this work, a repeated median filtering method is proposed for denoising digital mammogram images. A number of experiments are conducted on a dataset of different mammogram images to evaluate the proposed method using a set of image quality metrics. Experimental results are reported by computing the image quality metrics between the original clean images and denoised images that are corrupted by different levels of simulated speckle noise as well as salt and paper noise. Evaluation quality metrics showed that the repeated median filter method achieves a higher result than the related traditional median filter method.

Keywords: Mammogram images; image denoising; median filter; repeated median filtering; speckle noise; salt and paper noise

Hussain AlSalman, “A Repeated Median Filtering Method for Denoising Mammogram Images” International Journal of Advanced Computer Science and Applications(IJACSA), 11(11), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0111178

@article{AlSalman2020,
title = {A Repeated Median Filtering Method for Denoising Mammogram Images},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0111178},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0111178},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {11},
author = {Hussain AlSalman}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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