Evidence map›Paper›PMID 40996432›Full record

ArticleJournal of applied clinical medical physics2025

Human visual perception-based grayscale contrast enhancement with adaptive window settings in computed tomography images.

Wei Zhou, Yi Tian

Abstract read
In one paragraph

Article in Journal of applied clinical medical physics, 2025. 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

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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.

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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

2 authors.

Wei ZhouDepartment of Computed Tomography (CT) Concept, Siemens Shanghai Medical Equipment Ltd., Shanghai, China.ORCID https://orcid.org/0009-0001-8245-0173
Yi TianDepartment of Computed Tomography (CT) Concept, Siemens Shanghai Medical Equipment Ltd., Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGrayscale contrast plays a significant role in medical imaging. This is due to the fact that visual examination, where images are represented with different gray shades on display devices, is essential in current medical applications. PURPOSE: The effective contrast enhancement should have the ability to provide sufficient grayscale contrast and natural image appearance in line with human visual perception (HVP). To achieve the goal, the paper proposes a novel method for grayscale contrast improvement in computed tomography (CT) diagnostics.

methodsA new quantitative physical metric incorporating certain human visual characteristics, termed as contrast-perceived to spatial frequency ratio (CPSFR), is presented to assess the perceptual quality of grayscale-based images. The window settings achieved by maximizing CPSFR, Window Settings Automatic, was evaluated and compared with Window Settings Preset and Window Settings Manual both of which are most used in CT diagnostics in terms of target detectability and diagnostic satisfaction. Experiments were carried out with 720 phantom images and 80 patient images. In phantom study, images were acquired with the routine body protocol at varying dose levels and assessed by four CT physicists for identifying signal-present or signal-absent. In clinical study, images with liver lesions were evaluated and rated by three radiologists with a 4-point diagnostic quality score.

resultsThe phantom study indicated a statistically significant improvement with Window Settings Automatic, as compared to Window Settings Preset and Window Settings Manual (all p < 0.01) in accuracy and sensitivity for consensus readings. And the clinical study demonstrated Window Settings Automatic had the distinct advantage over other candidates (highest mean score 3.0; > 40% of all top-scoring votes).

conclusionsThe proposed method is capable of enhancing grayscale contrast in the region that really interests observers and yielding natural image appearance well suited to human visual perception to improve target detectability and diagnostic performance.

Indexed as

Image Processing, Computer-AssistedLiver NeoplasmsPhantoms, ImagingRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedVisual PerceptionAlgorithmsHumansCT image qualitydisplay windowinggrayscale contrast enhancementhuman visual perception

Identifiers

PMID40996432
PMCPMC12462540

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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.