Evidence map›Paper›PMID 36359581›Full record

ArticleDiagnostics (Basel, Switzerland)2022

New Real-Time Impulse Noise Removal Method Applied to Chest X-ray Images.

Nasr Rashid, Kamel Berriri, Mohammed Albekairi, Khaled Kaaniche, Ahmed Ben Atitallah, Muhammad Attique Khan, Osama I El-Hamrawy

Abstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Nasr RashidDepartment of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia.ORCID 0000-0003-4725-2072
Kamel BerririLAMMDA Laboratory, University of Sousse, Sousse 4054, Tunisia.
Mohammed AlbekairiDepartment of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia.
Khaled KaanicheDepartment of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia.ORCID 0000-0003-0625-6245
Ahmed Ben AtitallahDepartment of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia.ORCID 0000-0002-2121-4417
Muhammad Attique KhanDepartment of CS, HITEC University, Taxila 47080, Pakistan.ORCID 0000-0002-6347-4890
Osama I El-HamrawyDepartment of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia.ORCID 0000-0003-0421-587X

Funding

Deanship of Scientific Research at Jouf University DSR-2020-04-480
6 · The paper itself

Abstract

In this paper, we propose a new Modified Laplacian Vector Median Filter (MLVMF) for real-time denoising complex images corrupted by "salt and pepper" impulsive noise. The method consists of two rounds with three steps each: the first round starts with the identification of pixels that may be contaminated by noise using a Modified Laplacian Filter. Then, corrupted pixels pass a neighborhood-based validation test. Finally, the Vector Median Filter is used to replace noisy pixels. The MLVMF uses a 5 × 5 window to observe the intensity variations around each pixel of the image with a rotation step of π/8 while the classic Laplacian filters often use rotation steps of π/2 or π/4. We see better identification of noise-corrupted pixels thanks to this rotation step refinement. Despite this advantage, a high percentage of the impulsive noise may cause two or more corrupted pixels (with the same intensity) to collide, preventing the identification of noise-corrupted pixels. A second round is then necessary using a second set of filters, still based on the Laplacian operator, but allowing focusing only on the collision phenomenon. To validate our method, MLVMF is firstly tested on standard images, with a noise percentage varying from 3% to 30%. Obtained performances in terms of processing time, as well as image restoration quality through the PSNR (Peak Signal to Noise Ratio) and the NCD (Normalized Color Difference) metrics, are compared to the performances of VMF (Vector Median Filter), VMRHF (Vector Median-Rational Hybrid Filter), and MSMF (Modified Switching Median Filter). A second test is performed on several noisy chest x-ray images used in cardiovascular disease diagnosis as well as COVID-19 diagnosis. The proposed method shows a very good quality of restoration on this type of image, particularly when the percentage of noise is high. The MLVMF provides a high PSNR value of 5.5% and a low NCD value of 18.2%. Finally, an optimized Field-Programmable Gate Array (FPGA) design is proposed to implement the proposed method for real-time processing. The proposed hardware implementation allows an execution time equal to 9 ms per 256 × 256 color image.

Indexed as

cardiovascular diseases diagnosischest X-ray imagesFPGAHLSimage denoisingimpulsive noiseMLVMF

Identifiers

PMID36359581
PMCPMC9689277

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.