Multimodal medical image fusion based on joint bilateral filter and local gradient energy

As a powerful assistance technique for biomedical diagnosis, multimodal medical image fusion has emerged as a hot topic in recent years. Unfortunately, the trade-off among fusion performance, time consumption and noise robustness for many medical image fusion algorithms remains an enormous challenge. In this paper, an effective, fast and robust medical image fusion method is proposed. A two-layer decomposition scheme is introduced by the joint bilateral filter, the energy layer containing rich intensity information, and the structure layer capturing ample details. Then a novel local gradient energy operator based on the structure tensor and neighbor energy is proposed to fuse the structure layer and the l(1)-max rule is introduced to fuse the energy layer. A total of 118 co-registered pairs of medical images covering five different categories of medical image fusion problems are tested in experiments. Seven latest representative medical image fusion methods are compared, and six representative quality evaluation metrics with complementary characteristics are fully employed to objectively evaluate the fused results. Extensive experimental results demonstrate that the proposed method yields better performance than some state-of-the-art methods in both visual quality and quantitative evaluation, and achieves nearly real-time computational efficiency and robustness to noise. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

Multimodal medical image fusion is an important technique for biomedical diagnosis, and a novel method with two-layer decomposition and local gradient energy operator is proposed in this paper to achieve better fusion performance and computational efficiency. Extensive experiments demonstrate the method's superiority over state-of-the-art methods in both visual quality and quantitative evaluation.