Multi-modal brain image fusion based on multi-level edge-preserving filtering. (February 2021)

• Record Type: Journal Article
• Authors: Tan, Wei; Thitøn, William; Xiang, Pei; Zhou, Huixin
• Is Part Of: Biomedical signal processing and control. Volume 64(2021)
• Publication Date: 2021-02
• Subjects: Multi-modal medical imaging -- Medical image fusion -- Multi-level edge-preserving filtering -- Weighted mean curvature filtering -- Pulse-coupled neural network; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2020.102280
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: 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. Highlights: An effective image decomposition model called MLEPF is proposed. An Energy Attribute fusion strategy is applied in BS layers. A MLMG-PCNN fusion strategy is applied in FS and CS layers.