Multi-modal brain image fusion based on multi-level edge-preserving filtering

Recently, multi-modal medical imaging technology and its collaborative diagnosis technology are developing rapidly. The application of medical image fusion technology in medical diagnosis becomes more important. In this paper, a multi-modal medical image fusion algorithm based on multi-level edge-preserving filtering (MLEPF) decomposition model is proposed. Firstly, an MLEPF model based on weighted mean curvature filtering is presented and used to decompose the multi-modal medical image into three types of layers: fine-structure (FS), coarse-structure (CS), and base (BS) layers. Secondly, a gradient domain pulse-coupled neural network (PCNN) fusion strategy is used to merge the FS and CS layers, and an energy attribute fusion strategy is used to merge the BS layers. Finally, the fused image is obtained by combining the three types of fused layers. The experiments are performed on six different disease datasets and one normal dataset, which contains more than 100 image pairs. Qualitative and quantitative evaluation testify that the proposed algorithm is superior to some excellent algorithms and can achieve close result to some state-of-the-art algorithms.

Automated summary

This paper proposes a multi-modal medical image fusion algorithm based on multi-level edge-preserving filtering (MLEPF) decomposition model, which outperforms some excellent algorithms according to qualitative and quantitative evaluations.