Open AccessCT and MRI image fusion based on multiscale decomposition method and hybrid approach
Author(s) -
Chang Lihong,
Feng Xiangchu,
Zhu Xiaolong,
Zhang Rui,
He Ruiqiang,
Xu Chen
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5720
Subject(s) - contourlet , image fusion , artificial intelligence , computer science , fusion , computer vision , pattern recognition (psychology) , sparse approximation , image (mathematics) , norm (philosophy) , fusion rules , transformation (genetics) , magnetic resonance imaging , decomposition , radiology , wavelet transform , medicine , linguistics , philosophy , biochemistry , chemistry , political science , wavelet , gene , law , ecology , biology
In the fusion process of medical computed tomography (CT) and magnetic resonance image (MRI), traditional multiscale methods often reduce the contrast of fused images. Although sparse representation (SR) methods overcome this shortcoming, they are often too smooth along the strong edges of the fusion image. To overcome these shortcomings, CT and MRI image fusion based on multiscale decomposition method and hybrid approach is proposed. There are three main steps. First, the cartoon parts and texture parts of CT and MRI are obtained by the improved image decomposition method using global sparse gradients. Second, the large structure cartoon parts are fused using the specific cartoon dictionary and the ‘L1‐max norm’ principle. The textured parts are fused using non‐subsampled contourlet transformation (NSCT) and the maximum energy rule. Finally, the final result is obtained by superimposing the fused cartoon part and the fused texture part. The experimental results demonstrate that the proposed method outperforms the state‐of‐the‐art method SR and NSCT in terms of visual effect and objective quality.