A multi-modal brain image fusion technique using nakagami imaging and intuitionistic fuzzy sets

Abstract

Medical image fusion is essential for consolidating images from diverse modalities into a single image, offering comprehensive information for diagnosis and analysis. With the rapid evolution of brain imaging technologies, researchers increasingly focus on refining fuzzy fusion techniques to utilize the full potential of these modalities. However, existing fuzzy fusion approaches often face three key challenges: difficulty in enhancing low-contrast images, uncertainty in selecting appropriate fuzzy membership functions, and redundancy in preserving important features. To address these shortcomings, a Nakagami distribution and intuitionistic fuzzy set-based multi-modal brain image fusion (NIFMBF) framework is proposed. This method integrates three core innovations: First, Nakagami imaging (NI) is employed to enhance low-contrast areas and reveal imperceptible lesions; second, a novel intuitionistic fuzzy generator (IFG) is designed to transform NI outputs into intuitionistic fuzzy images that effectively handle vagueness and enhance fine structural details; third, gray-level co-occurrence matrix (GLCM)-based contrast feature extraction guides the fusion process, enabling the retention of crucial information while reducing redundant data. Extensive experiments conducted on three benchmark datasets and compared against five state-of-the-art fusion techniques demonstrate the superior performance of the proposed NIFMBF method. Quantitatively, the method achieves an average improvement of 12–20% for every considered fusion metric. Additionally, the proposed framework reduces computational time by nearly 30% highlighting its efficiency. These findings, validated by both qualitative and quantitative evaluations, underscore the efficacy and potential of NIFMBF for medical image fusion.