ArticleScientific reports2025
Optimized exposer region-based modified adaptive histogram equalization method for contrast enhancement in CXR imaging.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- Anatomy-Guided Hybrid CNN-ViT Model with Neuro-Symbolic Reasoning for Early Diagnosis of Thoracic Diseases Multilabel.Diagnostics (Basel, Switzerland) · 2026Article
- Adaptive Normalization Enhances the Generalization of Deep Learning Model in Chest X-Ray Classification.Journal of imaging · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In medical imaging, low-contrast chest X-ray (CXR) images may fail to provide adequate information for accurate visual interpretation and disease diagnosis. Conventional contrast enhancement techniques, such as histogram equalization, often introduce intensity shifts and loss of fine details. This study presents an advanced Exposure Region-Based Modified Adaptive Histogram Equalization (ERBMAHE) method, further optimized using Particle Swarm Optimization (PSO) to enhance contrast, preserve brightness, and strengthen fine details. The ERBMAHE method segments CXR images into underexposed, well-exposed, and overexposed regions using the 9IEC algorithm. The well-exposed region is further divided, generating five histograms. Each region undergoes adaptive contrast enhancement via a novel weighted probability density function (PDF) and power-law transformation to ensure balanced enhancement across different exposure levels. The PSO algorithm is then employed to optimize power-law parameters, further refining contrast enhancement and illumination uniformity while maintaining the natural appearance of medical images. The PSO-ERBMAHE method was tested on 600 Kaggle CXR images and compared against six state-of-the-art techniques. It achieved a superior peak signal-to-noise ratio (PSNR = 31.10 dB), entropy (7.48), feature similarity index (FSIM = 0.98), tenengrad function (TEN = 0.19), quality-aware relative contrast measure (QRCM = 0.10), and contrast ratio, while maintaining a low absolute mean brightness error (AMBE = 0.10). The method effectively enhanced image contrast while preserving brightness and visual quality, as confirmed by medical expert evaluations. The proposed PSO-ERBMAHE method delivers high-quality contrast enhancement in medical imaging, ensuring better visibility of critical anatomical features. By strengthening fine details, maintaining mean brightness, and improving computational efficiency, this technique enhances disease examination and diagnosis, reducing misinterpretation risks and improving clinical decision-making.
Indexed as
Identifiers
What Socratic holds
Registered trials
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 Socratic graph.