Revealing Historical Insights: A Comprehensive Exploration of Traditional Approaches in Medical Image Segmentation
Mudasir Ashraf, Majid Zaman · 2025
Medical image segmentation is a cornerstone in diagnostic and therapeutic procedures, providing crucial insights for precise clinical interventions. This paper offers a comprehensive exploration and critical analysis of traditional approaches employed in medical image segmentation, spanning the historical trajectory from heuristic methods to contemporary techniques. The review encompasses classical methodologies such as thresholding, region-based techniques, and contour-based algorithms, shedding light on their strengths, limitations, and evolution over time. By delving into the intricacies of traditional methods, this study aims to serve as a valuable resource for researchers, practitioners, and developers in the field. Furthermore, the paper lays the foundation for understanding the historical context of medical image segmentation, facilitating a more nuanced perspective in the era of evolving computational techniques and deep learning applications. A critical analysis compares the strengths and limitations of each approach, shedding light on their historical significance. The narrative extends to the modern era, highlighting how these traditional foundations paved the way for contemporary techniques. The paper concludes with insights into challenges faced by traditional methods, presenting opportunities for future advancements. This comprehensive review offers valuable historical perspectives crucial for understanding the trajectory of medical image segmentation and guiding the development of future methodologies.