Computed Tomography Images De-noising using a Novel Two Stage Adaptive Algorithm.

Mojtaba Fadaee, Mousa Shamsi, Hamidreza Saberkari, Mohammad Hossein Sedaaghi

Abstract read

In one paragraph

Article in Journal of medical signals and sensors. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it

0cells of the map it votes in

2citing 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

2 citing papers in PubMed.

A Novel Thresholding Based Algorithm for Detection of Vertical Root Fracture in Nonendodontically Treated Premolar Teeth.Journal of medical signals and sensors
Article
Quantitative Analysis of Image Quality in Low-Dose Computed Tomography Imaging for COVID-19 Patients.Journal of medical signals and sensors
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

4 authors.

Mojtaba FadaeeDepartment of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.

Mousa ShamsiDepartment of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.

Hamidreza SaberkariDepartment of Electrical Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran.

Mohammad Hossein SedaaghiDepartment of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, an optimal algorithm is presented for de-noising of medical images. The presented algorithm is based on improved version of local pixels grouping and principal component analysis. In local pixels grouping algorithm, blocks matching based on L (2) norm method is utilized, which leads to matching performance improvement. To evaluate the performance of our proposed algorithm, peak signal to noise ratio (PSNR) and structural similarity (SSIM) evaluation criteria have been used, which are respectively according to the signal to noise ratio in the image and structural similarity of two images. The proposed algorithm has two de-noising and cleanup stages. The cleanup stage is carried out comparatively; meaning that it is alternately repeated until the two conditions based on PSNR and SSIM are established. Implementation results show that the presented algorithm has a significant superiority in de-noising. Furthermore, the quantities of SSIM and PSNR values are higher in comparison to other methods.

Indexed as

AlgorithmsComputed Tomography ImagesNoisePrincipal Component AnalysisSignal-to-Noise Ratio

Identifiers

PMID26955565

PMCPMC4759838

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-SA

Read underepoch 390

Registered trials

None linked

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.