Laplacian Redecomposition for Multimodal Medical Image Fusion
| Autor: | Xiang Wang, Huaguang Li, Tao Luo, Pengfei Han, Xiaoxiao Li, Xiaopeng Guo |
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| Rok vydání: | 2020 |
| Předmět: |
Image fusion
Computer science business.industry 020208 electrical & electronic engineering ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition 02 engineering and technology Iterative reconstruction Field (computer science) Domain (software engineering) Distortion 0202 electrical engineering electronic engineering information engineering Artificial intelligence Noise (video) Electrical and Electronic Engineering business Instrumentation Laplace operator |
| Zdroj: | IEEE Transactions on Instrumentation and Measurement. 69:6880-6890 |
| ISSN: | 1557-9662 0018-9456 |
| Popis: | The field of multimodal medical image fusion has made huge progress in the past decade. However, previous methods always suffer from color distortion , blurring , and noise . To address these problems, we propose a novel Laplacian redecomposition (LRD) framework tailored to multimodal medical image fusion in this article. The proposed LRD has two technical innovations. First, we present a Laplacian decision graph decomposition scheme with image enhancement to obtain complementary information, redundant information, and low-frequency subband images. Second, considering the heterogeneous characteristics of redundant and complementary information, we introduce the concept of the overlapping domain (OD) and non-OD (NOD), where the OD contributes to fuse redundant information while the NOD is responsible for fusing complementary information. In addition, an inverse redecomposition scheme is given by leveraging the global decision graph and local mean to reconstruct high-frequency subband fusion images. Finally, the inverse Laplacian transform is applied to generate the fusion result. Experimental results demonstrate that the proposal outperforms other current popular fusion methods qualitatively and quantitatively. |
| Databáze: | OpenAIRE |
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