Multi-modal Medical Image Fusion Approach Utilizing Gradient Domain Guided Image Filtering

Abstract

Background: Currently, most multimodal medical image fusion techniques focus solely on integrating the edge details of image features, often overlooking color preservation from the source images. Hence, this paper proposes a multi-channel fusion algorithm based on gradient domain-guided image filtering.

Purpose: This study aims to enhance the color preservation of source images in multimodal medical image fusion algorithms.

Methods: Utilizing gradient field-guided image filters for image smoothing, the process involves constructing different image layers, decomposing using wavelet transforms, and downsampling. Various fusion rules are then applied before inverse wavelet transformation.

Results: Regarding MSE, CCI, PSNR, SSIM, DD, SM, and other metrics, the proposed algorithm consistently ranks highest compared to alternative methods.

Conclusion: Through both subjective and objective analyses, experimental results substantiate the significant edge-preserving effects of the proposed fusion algorithm while effectively maintaining image fidelity and spectral integrity.

Keywords: Bayesian estimation; Fuzzy logic.; Guided filtering; Image fusion; Multiscale decomposition.