Medical Image Fusion Based on Sparse Representation with KSVD
YU Nan-nan1
QIU Tian-shuang2
LIU Wen-hong3
1.Dalian University of Technology,Dalian 116086,China;Northeast Petroleum University,Daqing 163318,China2.Dalian University of Technology,Dalian 116086,China3.School of Electronic Information,Shanghai Dianji University,Shanghai 201306,China
摘要：Medical image fusion is a process by which two different models of im-ages are combined into a single image, in order to provide doctors with accurate diag-noses, and take right action. This paper proposes an image fusion method based on sparse representation with KSVD. Firstly, all source images are combined into a joint-matrix, which can be represented with sparse coefficients using an overcompletedictionary trained by KSVD algorithm. Secondly, the coefficients which are considered as image features are combined with the choose-max fusion rule. Finally, the fused image is reconstructed from the concatenated coefficients and the overcomplete dictionary. Compared with three state-of-the-art algorithms, the proposed method has better fusion performance.
机标关键词：
分类号：TP391(计算技术、计算机技术)
论文发表日期：2019-12-30
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：5( 168-172 )
英文信息