Medical image fusion method by using Laplacian pyramid and convolutional sparse representation. (20th December 2019)

• Record Type: Journal Article
• Authors: Liu, Feiqiang; Chen, Lihui; Lu, Lu; Ahmad, Awais; Jeon, Gwanggil; Yang, Xiaomin
• Other Names: Jeon Gwanggil guestEditor.; Bellandi Valerio guestEditor.; Bakhouya Mohamed guestEditor.; Zbakh Mostapha guestEditor.; Essaaidi Mohamed guestEditor.; Manneback Pierre guestEditor.
• Is Part Of: Concurrency and computation. Volume 32:Number 17(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-12-20
• Subjects: convolutional sparse representation -- Laplacian pyramid -- medical image fusion; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5632
• Languages: English
• ISSNs: 1532-0626

Abstract

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.