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Medical image fusion method by using Laplacian pyramid and convolutional sparse representation
Author(s) -
Liu Feiqiang,
Chen Lihui,
Lu Lu,
Ahmad Awais,
Jeon Gwanggil,
Yang Xiaomin
Publication year - 2020
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5632
Subject(s) - pyramid (geometry) , image fusion , artificial intelligence , image (mathematics) , computer science , fusion rules , pattern recognition (psychology) , representation (politics) , computer vision , sparse approximation , fusion , inverse , mathematics , linguistics , philosophy , geometry , politics , political science , law
Summary Medical image fusion is a technology of combining multi‐modal images to generate a composite image, which is favorable to improve the capability of doctors in diagnosis and treatment of the disease. In order to achieve good performance, a fusion method by combining Laplacian pyramid (LP) and convolutional sparse representation (CSR) is proposed. In the proposed fusion method, LP transform is performed on each pair of pre‐registered computed tomography image and magnetic resonance image to obtain their detail layers and base layer. Then, the base layer is fused with a CSR‐based approach, whereas the detail layers are merged using the popular “max‐absolute” rule. Finally, the fused image is reconstructed by performing the inverse LP transform over the fused base layer and detail layers. The advantages of our method are that the texture detail information contained in source images can be fully extracted and the overall contrast of the final fused image will not be decreased. Experimental results demonstrate the superiority of the proposed method.