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Medical image fusion based on NSCT and sparse representation
Shen, Chao1,2; Gao, Wei1; Ma, Caiwen1; Song, Zongxi1; Yin, Fei2; Dan, Lijun1; Wang, Fengtao1
2018
Conference Name10th International Conference on Digital Image Processing, ICDIP 2018
Source PublicationTenth International Conference on Digital Image Processing, ICDIP 2018
Volume10806
Conference Date2018-05-11
Conference PlaceShanghai, China
PublisherSPIE
Contribution Rank1
Abstract

Image fusion is to get a fused image that contains all important information from source images of the same scene. Meanwhile, multi-scale transforms and sparse representation (SR) are the two most effective techniques for image fusion. However, the SR-based image fusion methods are time-consuming and do not take the structural information of the source images into consideration. In addition, different multi-scale transform-based methods have their inevitable defects waiting to be solved till now. Therefore, in this paper, a new image fusion method combining nonsubsampled contourlet transform (NSCT) with SR is proposed. A decision map for the low-frequency coefficients according to the high-frequency coefficients is made to overcome these problems. Furthermore, it can reduce the calculation cost of the fusion algorithm and retain the useful information of source images as far as possible. Comparing with conventional multi-scale transform based methods and sparse representation based methods with a fixed or learned dictionary, the proposed method has better fusion performance in the field of medical image fusion. © 2018 SPIE.

KeywordNonsubsampled Contourlet K-svd Decision Map Medical Image Fusion
Department空间光学应用研究室
DOI10.1117/12.2503126
Indexed ByEI ; CPCI
ISBN9781510621992
Language英语
ISSN0277786X
WOS IDWOS:000452819600200
EI Accession Number20183605779292
Citation statistics
Document Type会议论文
Identifierhttp://ir.opt.ac.cn/handle/181661/30611
Collection空间光学应用研究室
Corresponding AuthorGao, Wei
Affiliation1.Xi'an Institute of Optics and Precision Mechanics of CAS, Xi'an; 710071, China;
2.University of Chinese Academy of Science, Beijing; 100049, China
Recommended Citation
GB/T 7714
Shen, Chao,Gao, Wei,Ma, Caiwen,et al. Medical image fusion based on NSCT and sparse representation[C]:SPIE,2018.
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