Advancements in Texture Analysis and Classification: A Bibliometric Review of Entropy-Based Approaches

Entropy-based texture analysis has gained significant attention in medical imaging, computer vision, and material science. The purpose of this paper is to provide a bibliometric review that maps the evolution, key contributors, research trends, and emerging themes of entropy-based texture analysis from 1980 to 2025. Using the Scopus database, 1,482 articles were initially retrieved and refined to 1,226 documents for analysis. VOSviewer was employed for bibliometric mapping, examining publication trends, authorship networks, keyword co-occurrence, and citation patterns. Results indicate a notable increase in research activity between 2004 and 2021, followed by a decline in recent years. The analysis highlights leading contributors, with significant work focusing on medical imaging applications such as radiomics and tumor heterogeneity assessment. While Shannon entropy remains widely used, newer measures like sample entropy, permutation entropy, and dispersion entropy are gaining attention. The study also identifies major research clusters, demonstrating the interdisciplinary nature of entropy-based texture analysis across medicine, engineering, and artificial intelligence. Despite database and language limitations, this review provides valuable insights into the field’s evolution and future directions, encouraging further interdisciplinary collaborations and advancements.