A Comprehensive Review and Algorithmic Analysis of Histogram-Based Contrast Enhancement Techniques for Medical Imaging

Saira Ali Bhatti¹, Maqbool Khan^2,*, Arshad Ahmad³, Muhammad Shahid Anwar⁴, Leila Jamel⁵, Aisha M. Mashraqi⁶, Wadee Alhalabi^7,*
1 School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad, Pakistan
2 College of Engineering and Computing, American University of Bahrain, Riffa, Bahrain
3 Faculty of Computing and IT, Sohar University, Sohar, Sultanate of Oman
4 IRC for Finance and Digital Economy, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
5 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
6 Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia
7 Immersive Virtual Reality Research Group, Department of Computer Science, King Abdulaziz University, Jeddah, Saudi Arabia
* Corresponding Author: Maqbool Khan. Email: email ; Wadee Alhalabi. Email: email

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2026.074688

Received 16 October 2025; Accepted 09 February 2026; Published online 11 March 2026

Download PDF

Abstract

Medical imaging is essential in modern health care, allowing accurate diagnosis and effective treatment planning. These images, however, often demonstrate low contrast, noise, and brightness distortion that reduce their diagnostic reliability. This review presents a structured and comprehensive analysis of advanced histogram equalization (HE)-based techniques for medical image enhancement. Our review methodology encompasses: (1) classical HE approaches and related limitations in medical domains; (2) adaptive schemes like Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogrma Equalization (CLAHE) and their advance variants; (3) brightness-preserving schemes like BBHE and MMBEBHE and related algorithms; (4) dynamic and recursive histogram equalization methods incorporating DHE and RMSHE; (5) fuzzy logic-based enhancement methodologies addressing uncertainty and noise in medical images; and (6) hybrid optimization methodologies through the application of metaheuristic algorithms (World Cup Optimization, Particle Swarm Optimization, Genetic Algorithms, along with histogram-based methodologies.) There is also a comparative discussion given based on contrast improvement, image brightness preservation, noise management, and computational efficiency. Such advancements have better capabilities of improving image quality, which is more important for improved diagnosis and image analysis.

Graphical Abstract

A Comprehensive Review and Algorithmic Analysis of Histogram-Based Contrast Enhancement Techniques for Medical Imaging

Keywords

Medical imaging; image enhancement techniques; histogram equalization; contrast enhancement; noise reduction; brightness preservation; diagnostic accuracy

Downloads
- Full-Text PDF
Citation Tools
- BibTex
- EndNote
- RIS

255

View
44

Download
0

Like

An Effective Machine-Learning Based Feature Extraction/Recognition Model for Fetal Heart Defect Detection from 2D Ultrasonic Imageries
Bingzheng Wu, Peizhong Liu, Huiling...
Vibration and Sound Radiation of Cylindrical Shell Covered with a Skin Made of Micro Floating Raft Arrays Excited by Turbulence
Dan Zhao, Qiong Wu, Minyao Gan,...
An Interpretable CNN for the Segmentation of the Left Ventricle in Cardiac MRI by Real-Time Visualization
Jun Liu, Geng Yuan, Changdi Yang,...
The Flipping-Free Full-Parallax Tabletop Integral Imaging with Enhanced Viewing Angle Based on Space-Multiplexed Voxel Screen and Compound Lens Array
Peiren Wang, Jinqiang Bi, Zilong...
An Improved Lung Cancer Segmentation Based on Nature-Inspired Optimization Approaches
Shazia Shamas, Surya Narayan Panda,...

A Comprehensive Review and Algorithmic Analysis of Histogram-Based Contrast Enhancement Techniques for Medical Imaging

Abstract

Graphical Abstract

Keywords

255

44

0

Further Information

Guidelines

Follow Us

Join Us

Contact Us

WhatsApp:

Share Link