Evidence map›Paper›PMID 34940261›Full record

ArticleBiosensors2021

Objective Numerical Evaluation of Diffuse, Optically Reconstructed Images Using Structural Similarity Index.

Vicky Mudeng, Minseok Kim, Se-Woon Choe

Abstract read
In one paragraph

Article in Biosensors, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Vicky MudengDepartment of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39253, Korea.ORCID 0000-0003-4970-0289
Minseok KimDepartment of Mechanical System Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea.ORCID 0000-0003-4539-4309
Se-Woon ChoeDepartment of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39253, Korea.ORCID 0000-0002-7298-4147

Funding

Brain Korea 21 FOUR Project Dept. of Aeronautics, Mechanical and Electronic Con-vergence Engineering, Kumoh National Institute of TechnologyBrain Korea 21 FOUR Project Dept. of IT Convergence Engineering, Kumoh National Institute of TechnologyNational Research Foundation of Korea NRF-2019R1F1A1062397National Research Foundation of Korea NRF-2020R1I1A307409812
6 · The paper itself

Abstract

Diffuse optical tomography is emerging as a non-invasive optical modality used to evaluate tissue information by obtaining the optical properties' distribution. Two procedures are performed to produce reconstructed absorption and reduced scattering images, which provide structural information that can be used to locate inclusions within tissues with the assistance of a known light intensity around the boundary. These methods are referred to as a forward problem and an inverse solution. Once the reconstructed image is obtained, a subjective measurement is used as the conventional way to assess the image. Hence, in this study, we developed an algorithm designed to numerically assess reconstructed images to identify inclusions using the structural similarity (SSIM) index. We compared four SSIM algorithms with 168 simulated reconstructed images involving the same inclusion position with different contrast ratios and inclusion sizes. A multiscale, improved SSIM containing a sharpness parameter (MS-ISSIM-S) was proposed to represent the potential evaluation compared with the human visible perception. The results indicated that the proposed MS-ISSIM-S is suitable for human visual perception by demonstrating a reduction of similarity score related to various contrasts with a similar size of inclusion; thus, this metric is promising for the objective numerical assessment of diffuse, optically reconstructed images.

Indexed as

Image Processing, Computer-AssistedTomography, OpticalAlgorithmsHumansbiosensorsdiffuse optical tomographyhuman visible perceptionnumerical evaluationstructural similarity

Identifiers

PMID34940261
PMCPMC8699273

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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.