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| Medical Image Fusion Based on Sparse Representation with KSVD |
| YU Nan-nan1, 2, QIU Tian-shuang1, LIU Wen-hong3 |
1. Dalian University of Technology, Dalian 116086, China;
2. Northeast Petroleum University, Daqing 163318, China;
3. School of Electronic Information, Shanghai Dianji University, Shanghai 201306, China |
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Abstract Medical image fusion is a process by which two different models of images are combined into a single image, in order to provide doctors with accurate diagnoses, 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.
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Received: 12 October 2019
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| Fund:National Science Foundation of China; grant number: 61172108, 61139001, 60872122; grant sponsor: Shanghai Dianji University Leading Academic Discipline Project; grant number: 10xkf01 |
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Corresponding Authors:
QIU Tian-shuang. E-mail: qiutsh @dlut.edu.cn
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