Work

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

Year: 2024

Type: article

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.

Source: Current Medical Imaging Formerly Current Medical Imaging Reviews

DOI: 10.2174/0115734056325441241022085037

Authors: Menghui Sun, Xiaoliang Zhu, Yunzhen Niu, Yang Li, Mengke Wen

Institutions: Xinjiang University, Xinjiang Medical University, Tumor Hospital of Xinjiang Medical University

Language: English

Topic: Advanced Image Fusion Techniques

Subfield: Media Technology

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Funder: Xinjiang University

Awards: G3507899275, G5940958786