A Stable Region-Based Image Segmentation Model Integrating Fuzzy Logic and Geometric Principles

Image segmentation remains a foundational task in computer vision, remote sensing, medical imaging, and object detection, serving as a critical step in delineating object boundaries and extracting meaningful regions from complex visual data.However, conventional segmentation methods often exhibit limited robustness in the presence of noise, intensity inhomogeneity, and intricate region geometries.To address these challenges, a novel segmentation framework was developed, integrating fuzzy logic with geometric principles.Uncertainty and overlapping intensity distributions within regions were modeled through fuzzy membership functions, allowing for more flexible and resilient region characterization.Simultaneously, geometric principles-specifically image gradients and curvature-were incorporated to guide boundary evolution, thereby improving delineation precision.A fuzzy energy functional was constructed to jointly optimize region homogeneity, edge preservation, and boundary smoothness.This functional was minimized through an iterative level-set evolution process, allowing dynamic adaptation to varying image characteristics while maintaining computational efficiency.The proposed model demonstrated robust performance across diverse image modalities, including those with high noise levels and complex regional structures, outperforming traditional methods in terms of segmentation accuracy and stability.Its applicability to tasks demanding high-precision region-based analysis highlights its potential for widespread deployment in advanced imaging applications.