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A Review of Multimodal Medical Image Fusion Techniques
Bing Huang1, Feng Yang1,2, Mengxiao Yin1,2
1School of Computer and Electronics and Information, Guangxi University, Nanning, Guangxi 530004, China.
Summary
Multimodal medical image fusion combines images from different sources to enhance clinical information. Recent advances, particularly deep learning methods, show significant progress but face ongoing research challenges.
Area of Science:
- Medical Imaging
- Computer Vision
- Artificial Intelligence
Background:
- Medical image fusion integrates data from diverse imaging modalities.
- This process aims to create a comprehensive image for improved clinical diagnosis and treatment planning.
- Existing fusion techniques vary in complexity and effectiveness.
Purpose of the Study:
- To provide a comprehensive overview of multimodal medical image fusion methods.
- To highlight recent advancements, focusing on deep learning approaches.
- To analyze current fusion techniques, imaging modalities, and performance evaluation datasets.
Main Methods:
- Review of current multimodal medical image fusion techniques.
- Emphasis on deep learning-based fusion strategies.
- Analysis of various medical imaging modalities used in fusion.
Main Results:
- Significant advancements observed in multimodal medical image fusion research.
- Deep learning methods are emerging as a key area of development.
- Performance analysis indicates a growing trend in the field.
Conclusions:
- Multimodal medical image fusion research is yielding significant results.
- The field is trending upwards with increasing development.
- Despite progress, substantial research challenges remain in multimodal medical image fusion.
