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X-ray Imaging

X-ray Imaging

German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...

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X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

Published on: September 11, 2011

Optimized exposer region-based modified adaptive histogram equalization method for contrast enhancement in CXR

Shivam Gangwar¹, Reeta Devi², Nor Ashidi Mat Isa³

¹Department of Electronics and Communication Engineering, University Institute of Engineering & Technology, Kurukshetra University, Kurukshetra, Haryana, 136119, India.

Scientific Reports

|February 25, 2025

Summary

This study introduces a new method for enhancing low-contrast chest X-rays (CXR) using Exposure Region-Based Modified Adaptive Histogram Equalization (ERBMAHE) optimized with Particle Swarm Optimization (PSO). The advanced technique improves image contrast and detail visibility for better disease diagnosis.

Keywords:

Adaptive HE CXR ERBMAHE Exposer-region Non-uniform illumination PSO

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Last Updated: May 25, 2025

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Area of Science:

Medical Imaging
Image Processing
Computational Intelligence

Background:

Low-contrast chest X-ray (CXR) images pose challenges for accurate diagnosis.
Conventional contrast enhancement methods can degrade image quality and obscure details.

Purpose of the Study:

To develop and validate an advanced contrast enhancement method for CXR images.
To improve the visibility of anatomical features and aid in disease diagnosis.

Main Methods:

Exposure Region-Based Modified Adaptive Histogram Equalization (ERBMAHE) segments images into exposure regions.
A novel weighted probability density function (PDF) and power-law transformation are applied for adaptive enhancement.
Particle Swarm Optimization (PSO) is used to fine-tune enhancement parameters for optimal results.

Main Results:

The PSO-ERBMAHE method demonstrated superior performance in metrics like PSNR, entropy, FSIM, and contrast ratio.
It effectively enhanced contrast while preserving brightness and fine details, confirmed by expert evaluation.
Achieved a low absolute mean brightness error (AMBE) of 0.10.

Conclusions:

The PSO-ERBMAHE method offers high-quality contrast enhancement for medical imaging.
It improves the visibility of critical anatomical features, reducing misinterpretation risks.
This technique enhances disease examination and supports clinical decision-making.