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Reseach Article

Performance Analysis of Different Filters for De-Noising Medical Images

Published on September 2016 by Baishali Goswami, Santanu Kr. Misra
International Conference on Computing and Communication
Foundation of Computer Science USA
ICCC2016 - Number 1
September 2016
Authors: Baishali Goswami, Santanu Kr. Misra
79633292-3628-45cf-8fec-a348b99144d4

Baishali Goswami, Santanu Kr. Misra . Performance Analysis of Different Filters for De-Noising Medical Images. International Conference on Computing and Communication. ICCC2016, 1 (September 2016), 1-5.

@article{
author = { Baishali Goswami, Santanu Kr. Misra },
title = { Performance Analysis of Different Filters for De-Noising Medical Images },
journal = { International Conference on Computing and Communication },
issue_date = { September 2016 },
volume = { ICCC2016 },
number = { 1 },
month = { September },
year = { 2016 },
issn = 0975-8887,
pages = { 1-5 },
numpages = 5,
url = { /proceedings/iccc2016/number1/26151-cc52/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Computing and Communication
%A Baishali Goswami
%A Santanu Kr. Misra
%T Performance Analysis of Different Filters for De-Noising Medical Images
%J International Conference on Computing and Communication
%@ 0975-8887
%V ICCC2016
%N 1
%P 1-5
%D 2016
%I International Journal of Computer Applications
Abstract

Image brightness is generally desirable to be uniform except regions where it changes to form an image. There are factors, however, that tend to produce variation in the brightness of a displayed image even when no image detail is present. This variation is usually random and has no particular pattern. In many cases, it reduces image quality. This random variation in image brightness is designated as noise. In this experimental work, different medical images like MRI, Cancer, X-ray, and Brain images have been considered and have been then used to calculate thestandard deviation and mean of all these images after finding Speckle noise and applyingvarious filtering techniques for removal of noise. This experimental analysis will improve the accuracy of these medical images for easy diagnosis. The results, which have been achieved, are more useful and they prove to be helpful for general medical practitioners to analyze the symptoms of the patient.

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Index Terms

Computer Science
Information Sciences

Keywords

Mri – Magnetic Resonance Imaging X-ray ct Median Filter Adaptive Filter And Average Filter.