@Article{cmes.2026.074688,
AUTHOR = {Saira Ali Bhatti, Maqbool Khan, Arshad Ahmad, Muhammad Shahid Anwar, Leila Jamel, Aisha M. Mashraqi, Wadee Alhalabi},
TITLE = {A Comprehensive Review and Algorithmic Analysis of Histogram-Based Contrast Enhancement Techniques for Medical Imaging},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {146},
YEAR = {2026},
NUMBER = {3},
PAGES = {--},
URL = {http://www.techscience.com/CMES/v146n3/66788},
ISSN = {1526-1506},
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.},
DOI = {10.32604/cmes.2026.074688}
}