NFSAOA: a new approach of medical brain image fusion using neutrosophic fuzzy set and arithmetic optimization algorithm.

Diagnostic accuracy in medical imaging depends on high-quality multimodal image fusion (MMIF). It improves image quality by combining data from diverse imaging modalities. However, many existing fusion approaches fail to capture distinct information from diverse modalities, resulting in an incomplete fused image and potentially a misleading, incorrect diagnosis. To address these issues, we propose a new approach to the MMIF framework that incorporates a neutrosophic fuzzy set (NFS) with a feature extraction and arithmetic optimization algorithm (AOA), which are designed to eliminate the uncertainty and indeterminacy present in medical modalities. In the first phase, we utilized a Gaussian filter to decompose the source modalities into distinct layers, including the base and detail. In the second phase, the detail layers were fused with the optimal weights, generated by the AOA fusion strategy, which preserved the significant edge features and retained the comprehensive information. In the third phase, the base layers were converted into neutrosophic fuzzy images (NFI) using a neutrosophic fuzzy set. Thereafter, the α-mean and β-enhancement operations were employed to enhance the quality of the NFI images. Then, the Tamura feature extractions and contrast visibility enhancements were implemented for the extraction of significant features from the modalities of the base layers. In the last phase, the final resultant image was obtained by integrating the fused detail and base components. Experimental outcomes reveal that the presented method outperforms other fusion approaches. The qualitative and objective evaluation demonstrates that the proposed approach yields a composite image with an excellent visual appearance and contrast without artifacts. Moreover, this developed method consistently yields better objective values while maintaining reasonable runtime analysis, thereby balancing efficiency as well as fusion performance.