Multi-level fuzzy contourlet-based image fusion for medical applications

Multi-modal images fusion is one of the most truthful and useful diagnostic techniques in medical imaging system. This study proposes an image fusion system for medical engineering based on contourlet transform and multi-level fuzzy reasoning technique in which useful information from two spatially registered medical images is integrated into a new image that can be used to make clinical diagnosis and treatment more accurate. The system applies pixel-based fuzzy fusion rule to contourlet's coefficients of high-frequency details and feature-based fuzzy fusion to its low-frequency approximations, which can help the development of sophisticated algorithms that consider not only the time cost but also the quality of the fused image. The developed fusion system eliminates undesirable effects such as fusion artefacts and loss of visually vital information that compromise their usefulness by means of taking into account the physical meaning of contourlet coefficients. The experimental results show that the proposed fusion system outperforms the existing fusion algorithms and is effective to fuse medical images from different sensors with applications in brain image processing.