Enhancing Image Segmentation Using Generalized Convex Fuzzy Sets and Statistical Consistency
Year: 2025
Type: article
Abstract: The accurate segmentation of visual data into semantically meaningful regions remains a critical task across diverse domains, including medical diagnostics, satellite imagery interpretation, and automated inspection systems, where precise object delineation is essential for subsequent analysis and decision-making.Conventional segmentation techniques often suffer from limitations such as sensitivity to noise, intensity inhomogeneity, and weak boundary definition, resulting in reduced performance under complex imaging conditions.Although fuzzy set-based approaches have been proposed to improve adaptability under uncertainty, they frequently fail to maintain a balance between segmentation precision and robustness.To address these challenges, a novel segmentation framework was developed based on Pythagorean Fuzzy Sets (PyFSs) and local averaging, offering enhanced performance in uncertain and heterogeneous visual environments.By incorporating both membership and non-membership degrees, PyFSs allow a more flexible representation of uncertainty compared to classical fuzzy models.A local average intensity function was introduced, wherein the contribution of each pixel was adaptively weighted according to its PyFS membership degree, improving resistance to local intensity variations.An energy functional was formulated by integrating PyFS-driven intensity constraints, local statistical deviation measures, and regularization terms, ensuring precise boundary localization through level set evolution.Convexity of the energy formulation was analytically demonstrated to guarantee the stability of the optimization process.Experimental evaluations revealed that the proposed method consistently outperforms existing fuzzy and non-fuzzy segmentation algorithms, achieving superior accuracy in applications such as medical image analysis and natural scene segmentation.These results underscore the potential of PyFS-based models as a powerful and generalizable solution for uncertainty-resilient image segmentation in real-world applications.
DOI: 10.56578/ida040105
Author: Zakir Husain
Institution: University of Peshawar
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4409578790